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Pipeline run

06196800-425c-4cc5-af85-e460a3dedce3

Pipeline LLM cost (USD)
API 1: $0.0102 API 2: $0.0005 API 3: $0.0000 Total: $0.0107

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Data Engineering / Data Platform
Own cross-org credit data strategy and build scalable, governed data products: write code for PB-scale pipelines, shape data models/taxonomies, and drive adoption of privacy-compliant solutions using AI, analytics, and enterprise governance tools.
""Write clean and solid code that scales over PB of data""
Tech stack maturity
Modern Cloud Native
The stack centers on GCP, BigQuery, Kafka, Airflow, Spark, and Python, which are characteristic of a modern cloud-native data engineering environment with AI/ML capabilities.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI, ML, Machine Learning, Artificial Intelligence
Evidence — skills matched in JD (25)
Python SQL BigQuery Airflow Spark Kafka GCP Machine Learning Artificial Intelligence Lambda Architecture Kappa Architecture Data Modeling Data Architecture Data Governance Enterprise Data Management Domain-Driven Design Streaming Batch Processing Feature Stores Model Monitoring ETL Privacy-by-Design Data Discovery Data Quality Monitoring ML-assisted ETL
Skill cluster (6 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Asynchronous Messaging and Event Streaming
Kafka
Cloud Provider Platforms
GCP
Python Programming
Python
Software Concepts, Patterns & Practices
Domain-Driven Design
Cross-cutting / unaligned
SQL BigQuery Airflow Spark Artificial Intelligence Lambda Architecture Kappa Architecture Data Modeling Data Architecture Data Governance Enterprise Data Management Streaming Batch Processing Feature Stores Model Monitoring ETL Privacy-by-Design Data Discovery Data Quality Monitoring ML-assisted ETL
Show KRA description ↓
What you need to know about the role- This role requires strong product sense to directly support the business. Scale data management capabilities and drive high-impact, cross-functional initiatives that shape the future of data at GCSC Risk. Provides technical leadership, business and technical strategic alignment, and advanced problem-solving at an org-wide level. To successfully execute on cross-org initiatives, we need someone who can innovate, drive technical strategy, and ensure that our data solutions are scalable, efficient, and aligned with GCSC Risk’s broader goals. This position will report to the Head of Credit Internal Data and will require a significant amount of collaboration across all business, technologies, product & platform teams within PayPal and its associated business entities. We are helping rethink how we use data to make credit decisions at PayPal to fulfill the needs of our customers (both internal and external). We drive the creation of delightful, frictionless, and compliant payment experiences while maximizing the value of our data. In this role you will get an opportunity to support the implementation and adoption of the Enterprise Data Management & Governance (EDG) program across the company. In your day to day role you will • Work independently or as a team member. Proactively remove obstacles to ensure timely delivery of product and goals • Write clean and solid code that scales over PB of data and enforce engineering excellence in the organization • Improve data management efficiency through AI capabilities, better process and best practices • Embed Privacy-by-Design principles into all data solutions and ensure compliance with regulatory requirements. • Provide expertise across the data product development lifecycle—spanning data engineering, architecture, and analytics—to design and deliver reusable, accessible, and high-quality data solutions. • Design data structures and taxonomies that support standardization, integration, and alignment with business processes. • Deliver technical leadership through analytical thinking, innovation, and detailed specifications. • Drive data product execution and adoption through a metrics-based approach • Define and communicate data strategy for credit products, aligned with PayPal’s technical direction, evolving credit strategies, and the external data ecosystem • Strong product sense to identify data challenges and opportunities, and assess the impact of data-driven solutions • Leverage enterprise frameworks, governance tools, and reusable architecture patterns for Credit Risk and cross organizations • Foster influential cross-functional relationships through collaboration, proactive planning, and decisive leadership to design scalable solutions across platforms and products. • Strong collaboration skills and ability to influence across all organizations and levels within the company • Ability to communicate clearly and succinctly to all levels within the organization- translating the organizations goals into execution plans & metrics • Possess the ability to connect, engage and lead with empathy • Motivate others through a shared vision and confidence that empowers employees and teams to perform at their best • Demonstrate ability to delegate work • Operate with transparency and honesty in all interactions • 12+ years of experience in enterprise data management, with deep expertise in data governance, architecture, modelling, and platform design • Proven experience enabling data products at scale within large, cross-functional organizations • Strong business acumen and interest to deeply understand of PayPal’s credit use cases, data producers, and consumer needs. • Strong technical acumen with the ability to quickly grasp the technical details of products and systems • Expertise in data modelling using domain-driven design principles and a strong grasp of data architecture best practices. • Proficient in building and optimizing data platforms and pipelines using streaming and batch processing, including Lambda and Kappa architectures • Technical proficiency data management stacks and techniques such as Python, SQL, BigQuery, Airflow, Spark, Kafka, and data modelling, with experience in integrating ML/AI into scalable data solutions. • Experience with cloud-based data platforms, preferably GCP, and associated services • Demonstrated success in supporting ML/AI workloads (e.g., feature stores, training pipelines, model monitoring) and integrating AI into data workflows • Familiarity with AI-enhanced data systems (e.g., automated data discovery, quality monitoring, ML-assisted ETL) • Able to independently drive complex and ambiguous problem-solving efforts, balancing technical and strategic trade-offs • Excellent verbal and written communication skills with the ability to collaborate effectively across engineering, risk, product, and compliance teams. • Adaptive, self-motivated, and comfortable navigating shifting priorities while maintaining focus on the end-to-end impact of data across systems. • Proven experience bridging the gap between data engineering and machine learning engineering. • Experience in financial services, with a strong preference for exposure to the credit risk domain.

Signals

Skill data-engineer
0.33
Alias
KRA data-engineer
0.58

Post-classification

Centroidupdated · n=3
Alias collision log
New-role queue
New skills captured15
New KRA capturedyes

Captured for admin review

Lambda Architecture primary Data Governance Engineer pending
Kappa Architecture primary Data Governance Engineer pending
Data Modeling primary Data Governance Engineer pending
Data Architecture primary Data Governance Engineer pending
Data Governance primary Data Governance Engineer pending
Enterprise Data Management primary Data Governance Engineer pending
Streaming primary Data Governance Engineer pending
Batch Processing primary Data Governance Engineer pending
Feature Stores Data Governance Engineer pending
Model Monitoring Data Governance Engineer pending
ETL Data Governance Engineer pending
Privacy-by-Design Data Governance Engineer pending
Data Discovery Data Governance Engineer pending
Data Quality Monitoring Data Governance Engineer pending
ML-assisted ETL Data Governance Engineer pending
R&R fragment (sim 0.00) Data Governance Engineer pending

What you need to know about the role- This role requires strong product sense to directly support the business. Scale data management capabilities and drive high-impact, cross-functional initiatives t…

Status: completed Created: 2026-05-27T14:49:45.334298Z Updated: 2026-06-12T17:15:17.840926Z API 3 duration: 31906 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Governance Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: data-governance-engineer · id: 146 · source: db

Domain=Data Engineering & Analytics; The JD centers on enterprise data management, governance, privacy/compliance, standardization, taxonomy, and reusable high-quality data solutions, which best matches a Data Governance Engineer.

Matched skills

AI capabilitiesPrivacy-by-DesignEnterprise Data Management & Governance (EDG)data engineeringarchitectureanalytics

Matched dimensions

Data governance and managementPrivacy and regulatory complianceData architecture and standardizationCross-functional technical leadershipData strategy for credit productsProduct-minded data solutions

Matched KRAs

Scale data management capabilitiesSupport implementation and adoption of EDG programEmbed Privacy-by-Design principlesDesign data structures and taxonomiesDrive data product execution and adoptionDefine and communicate data strategy for credit products

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
3
Skipped

Job description

The Company
PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy.

We operate a global, two-sided network at scale that connects hundreds of millions of merchants and consumers. We help merchants and consumers connect, transact, and complete payments, whether they are online or in person. PayPal is more than a connection to third-party payment networks. We provide proprietary payment solutions accepted by merchants that enable the completion of payments on our platform on behalf of our customers.

We offer our customers the flexibility to use their accounts to purchase and receive payments for goods and services, as well as the ability to transfer and withdraw funds. We enable consumers to exchange funds more safely with merchants using a variety of funding sources, which may include a bank account, a PayPal or Venmo account balance, PayPal and Venmo branded credit products, a credit card, a debit card, certain cryptocurrencies, or other stored value products such as gift cards, and eligible credit card rewards. Our PayPal, Venmo, and Xoom products also make it safer and simpler for friends and family to transfer funds to each other. We offer merchants an end-to-end payments solution that provides authorization and settlement capabilities, as well as instant access to funds and payouts. We also help merchants connect with their customers, process exchanges and returns, and manage risk. We enable consumers to engage in cross-border shopping and merchants to extend their global reach while reducing the complexity and friction involved in enabling cross-border trade.

Our beliefs are the foundation for how we conduct business every day. We live each day guided by our core values of Inclusion, Innovation, Collaboration, and Wellness. Together, our values ensure that we work together as one global team with our customers at the center of everything we do – and they push us to ensure we take care of ourselves, each other, and our communities.

Job Description Summary:
What you need to know about the role- This role requires strong product sense to directly support the business. Scale data management capabilities and drive high-impact, cross-functional initiatives that shape the future of data at GCSC Risk. Provides technical leadership, business and technical strategic alignment, and advanced problem-solving at an org-wide level. To successfully execute on cross-org initiatives, we need someone who can innovate, drive technical strategy, and ensure that our data solutions are scalable, efficient, and aligned with GCSC Risk’s broader goals. Meet our team As the Senior Staff Engineer, Global Risk Data Management within the Credit Infrastructure and Data organization, you will implement common data practices across functional areas that drives standardization, quality, ease of access, accelerates governance practices by treating data as a product and facilitates a data-driven culture across PayPal where data is considered a strategic asset and treated accordingly-with the proper controls and quality.

Job Description:
Your way to impact

This position will report to the Head of Credit Internal Data and will require a significant amount of collaboration across all business, technologies, product & platform teams within PayPal and its associated business entities.

We are helping rethink how we use data to make credit decisions at PayPal to fulfill the needs of our customers (both internal and external). We drive the creation of delightful, frictionless, and compliant payment experiences while maximizing the value of our data. In this role you will get an opportunity to support the implementation and adoption of the Enterprise Data Management & Governance (EDG) program across the company.

Your day to day

In your day to day role you will

• Work independently or as a team member. Proactively remove obstacles to ensure timely delivery of product and goals 
• Write clean and solid code that scales over PB of data and enforce engineering excellence in the organization 
• Improve data management efficiency through AI capabilities, better process and best practices 
• Embed Privacy-by-Design principles into all data solutions and ensure compliance with regulatory requirements. 
• Provide expertise across the data product development lifecycle—spanning data engineering, architecture, and analytics—to design and deliver reusable, accessible, and high-quality data solutions. 
• Design data structures and taxonomies that support standardization, integration, and alignment with business processes. 
• Deliver technical leadership through analytical thinking, innovation, and detailed specifications. 
• Drive data product execution and adoption through a metrics-based approach 
• Define and communicate data strategy for credit products, aligned with PayPal’s technical direction, evolving credit strategies, and the external data ecosystem 
• Strong product sense to identify data challenges and opportunities, and assess the impact of data-driven solutions 
• Leverage enterprise frameworks, governance tools, and reusable architecture patterns for Credit Risk and cross organizations 
• Foster influential cross-functional relationships through collaboration, proactive planning, and decisive leadership to design scalable solutions across platforms and products.



Technical Leadership Qualities:

• Strong collaboration skills and ability to influence across all organizations and levels within the company 
• Ability to communicate clearly and succinctly to all levels within the organization- translating the organizations goals into execution plans & metrics 
• Possess the ability to connect, engage and lead with empathy 
• Motivate others through a shared vision and confidence that empowers employees and teams to perform at their best 
• Demonstrate ability to delegate work 
• Operate with transparency and honesty in all interactions



What Do You Need To Bring-
• 12+ years of experience in enterprise data management, with deep expertise in data governance, architecture, modelling, and platform design 
• Proven experience enabling data products at scale within large, cross-functional organizations 
• Strong business acumen and interest to deeply understand of PayPal’s credit use cases, data producers, and consumer needs. 
• Strong technical acumen with the ability to quickly grasp the technical details of products and systems 
• Expertise in data modelling using domain-driven design principles and a strong grasp of data architecture best practices. 
• Proficient in building and optimizing data platforms and pipelines using streaming and batch processing, including Lambda and Kappa architectures 
• Technical proficiency data management stacks and techniques such as Python, SQL, BigQuery, Airflow, Spark, Kafka, and data modelling, with experience in integrating ML/AI into scalable data solutions. 
• Experience with cloud-based data platforms, preferably GCP, and associated services 
• Demonstrated success in supporting ML/AI workloads (e.g., feature stores, training pipelines, model monitoring) and integrating AI into data workflows 
• Familiarity with AI-enhanced data systems (e.g., automated data discovery, quality monitoring, ML-assisted ETL) 
• Able to independently drive complex and ambiguous problem-solving efforts, balancing technical and strategic trade-offs 
• Excellent verbal and written communication skills with the ability to collaborate effectively across engineering, risk, product, and compliance teams. 
• Adaptive, self-motivated, and comfortable navigating shifting priorities while maintaining focus on the end-to-end impact of data across systems.



Bonus Qualifications
• Proven experience bridging the gap between data engineering and machine learning engineering. 
• Experience in financial services, with a strong preference for exposure to the credit risk domain.



Education:

• BS or advanced degree in Engineering, Computer Science, or related technical field.



For the majority of employees, PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace, ensuring that you equally have the benefits and conveniences of both locations.

Our Benefits:

At PayPal, we’re committed to building an equitable and inclusive global economy. And we can’t do this without our most important asset—you. That’s why we offer benefits to help you thrive in every stage of life. We champion your financial, physical, and mental health by offering valuable benefits and resources to help you care for the whole you.

We have great benefits including a flexible work environment, employee shares options, health and life insurance and more. To learn more about our benefits please visit https://www.paypalbenefits.com

Who We Are:

To learn more about our culture and community visit https://about.pypl.com/who-we-are/default.aspx

Commitment to Diversity and Inclusion

PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at paypalglobaltalentacquisition@paypal.com.

Belonging at PayPal:

Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal.

Any general requests for consideration of your skills, please Join our Talent Community.

We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don’t hesitate to apply.

REQ ID R0125707

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
BigQuery Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: BigQuery id=106 · bigquery

Aliases — catalog

  • BigQuery (CANONICAL) primary

Context tags (catalog)

Cloud Storage Dataflow ELT ETL GCP Google Cloud Platform Looker Pub/Sub SQL Standard SQL clustered tables data warehouse dbt partitioned tables service account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Google
License
proprietary
Year introduced
2011
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Airflow Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Airflow id=265 · airflow

Aliases — catalog

  • Airflow (CANONICAL) primary
  • airflow 2 (VERSION)
  • airflow-2 (VERSION)
  • airflow2 (VERSION)
  • airflow2.x (VERSION)
  • apache airflow 2 (VERSION)

Context tags (catalog)

Apache Celery CeleryExecutor DAG ETL Executor Jinja templating Python SLA Sensors UI XCom backfill connections data pipeline executor hooks logging monitoring operators plugins scheduler task dependencies task instance variables

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
2.x

Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3533
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer

  • Messaging and Background Jobs Catalog dimension db id 291

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GCP id=186 · gcp

Aliases — catalog

  • GCP (CANONICAL) primary

Context tags (catalog)

Anthos App Engine Artifact Registry BigQuery Cloud Build Cloud Composer Cloud Functions Cloud Logging Cloud Monitoring Cloud Run Cloud SQL Cloud Spanner Cloud Storage Compute Engine Dataflow GKE IAM Kubernetes Pub/Sub Service Accounts Stackdriver Terraform VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Google
License
other_open
Year introduced
2011
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GCP appears frequently in cloud/platform job descriptions and is a major hyperscaler alongside AWS/Azure, with broad enterprise adoption and active vendor investment.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Artificial Intelligence Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Artificial Intelligence id=1357 · artificial-intelligence

Aliases — catalog

  • Artificial Intelligence (CANONICAL)

Context tags (catalog)

AI ethics PyTorch TensorFlow algorithm optimization computer vision data mining deep learning machine learning model training natural language processing neural networks predictive analytics reinforcement learning supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Artificial Intelligence
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (Microsoft, Google, AWS) have standardized AI offerings, signaling broad market adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1020
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Lambda Architecture Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Kappa Architecture Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain modeling id=2379 · domain-modeling

Aliases — catalog

  • domain modeling (CANONICAL) primary
  • Domain Modeling (CANONICAL)

Context tags (catalog)

CQRS DDD ERD UML aggregate bounded context business logic context map context mapping data modeling domain events domain-driven design entities entity event sourcing event storming microservices repositories repository pattern service layer services value object value objects

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Domain Modeling
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
2831
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Data Architecture Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Data Governance Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Enterprise Data Management Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Domain-Driven Design Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain-driven design id=90 · domain-driven-design

Aliases — catalog

  • Domain‐Driven Design (CANONICAL) primary
  • domain-driven design (CANONICAL)

Context tags (catalog)

CQRS Ubiquitous Language aggregate aggregate root anti-corruption layer anti-pattern bounded context context map domain event domain events domain model domain service entity event sourcing event storming hexagonal architecture microservices repository service layer strategic design tactical design ubiquitous language value object

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Domain Driven Design
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly requested in software architecture JDs and widely covered in enterprise engineering hiring; no vendor sunset, and it remains a standard design approach rather than a niche tool.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
43
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

  • Backend Application Architecture and Code Organization Catalog dimension db id 366

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Backend Application Architecture and Code Organization
backend-application-architecture-and-code-organization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Streaming Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Batch Processing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Feature Stores Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Feature Store id=1593 · feature-store

Aliases — catalog

  • Feature Store (CANONICAL)

Context tags (catalog)

ML model serving batch processing data governance data lineage data pipeline data quality data retrieval data versioning feature engineering feature retrieval integration metadata management model training real-time analytics schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Feature Store
Confidence
0.78
Version strategy
NOT_APPLICABLE

Maturity reasoning: Feature stores appear in growing MLOps job postings and vendor ecosystems (e.g., Feast, Tecton, SageMaker Feature Store), but they’re still far less universal than core datastores like PostgreSQL or Redis.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
1199
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Model Monitoring Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ETL Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Privacy-by-Design Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Discovery Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Quality Monitoring Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ML-assisted ETL Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Airflow in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Artificial Intelligence in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Domain-Driven Design in_db
Application Architecture Patterns
application-architecture-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Domain-Driven Design in_db
Backend Application Architecture and Code Organization
backend-application-architecture-and-code-organization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Domain-Driven Design in_db
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Feature Stores new
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Lambda Architecture | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Kappa Architecture | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Architecture | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Data Governance | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Enterprise Data Management | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Streaming | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Batch Processing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Model Monitoring | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Privacy-by-Design | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Discovery | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality Monitoring | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ML-assisted ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
dimension_skill_link_proposed Feature Stores ↔ React Frontend Development
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior Staff Engineer, Global Risk Data Management
CompanyPayPal
Experience12+ years of experience in enterprise data management
DomainFinancial Services
Location(hybrid)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "PayPal has been revolutionizing commerce",
      "last_5_words": "take care of ourselves, each other, and our communities."
    },
    "text": "PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. We operate a global, two-sided network at scale that connects hundreds of millions of merchants and consumers. We help merchants and consumers connect, transact, and complete payments, whether they are online or in person. PayPal is more than a connection to third-party payment networks. We provide proprietary payment solutions accepted by merchants that enable the completion of payments on our platform on behalf of our customers. We offer our customers the flexibility to use their accounts to purchase and receive payments for goods and services, as well as the ability to transfer and withdraw funds. We enable consumers to exchange funds more safely with merchants using a variety of funding sources, which may include a bank account, a PayPal or Venmo account balance, PayPal and Venmo branded credit products, a credit card, a debit card, certain cryptocurrencies, or other stored value products such as gift cards, and eligible credit card rewards. Our PayPal, Venmo, and Xoom products also make it safer and simpler for friends and family to transfer funds to each other. We offer merchants an end-to-end payments solution that provides authorization and settlement capabilities, as well as instant access to funds and payouts. We also help merchants connect with their customers, process exchanges and returns, and manage risk. We enable consumers to engage in cross-border shopping and merchants to extend their global reach while reducing the complexity and friction involved in enabling cross-border trade. Our beliefs are the foundation for how we conduct business every day. We live each day guided by our core values of Inclusion, Innovation, Collaboration, and Wellness. Together, our values ensure that we work together as one global team with our customers at the center of everything we do \u2013 and they push us to ensure we take care of ourselves, each other, and our communities.",
    "word_count": 309
  },
  "certifications": [],
  "company_name": "PayPal",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "FinTech",
        "Credit Risk"
      ],
      "domain": "Financial Services"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Engineering / Computer Science (or related)",
      "raw": "BS or advanced degree in Engineering, Computer Science, or related technical field",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 12,
    "raw": "12+ years of experience in enterprise data management"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": null,
      "state": null,
      "work_mode": "hybrid"
    }
  ],
  "role": "Senior Staff Engineer, Global Risk Data Management",
  "role_aliases": [
    "Senior Data Engineer",
    "Staff Data Engineer",
    "Data Management Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Job Description Summary",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "What you need to know",
        "last_5_words": "aligned with GCSC Risk\u2019s broader goals."
      },
      "text": "What you need to know about the role- This role requires strong product sense to directly support the business. Scale data management capabilities and drive high-impact, cross-functional initiatives that shape the future of data at GCSC Risk. Provides technical leadership, business and technical strategic alignment, and advanced problem-solving at an org-wide level. To successfully execute on cross-org initiatives, we need someone who can innovate, drive technical strategy, and ensure that our data solutions are scalable, efficient, and aligned with GCSC Risk\u2019s broader goals.",
      "word_count": 83
    },
    {
      "bullet_count": 0,
      "heading": "Your way to impact",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "This position will report to",
        "last_5_words": "adoption of the Enterprise Data Management"
      },
      "text": "This position will report to the Head of Credit Internal Data and will require a significant amount of collaboration across all business, technologies, product \u0026 platform teams within PayPal and its associated business entities. We are helping rethink how we use data to make credit decisions at PayPal to fulfill the needs of our customers (both internal and external). We drive the creation of delightful, frictionless, and compliant payment experiences while maximizing the value of our data. In this role you will get an opportunity to support the implementation and adoption of the Enterprise Data Management \u0026 Governance (EDG) program across the company.",
      "word_count": 92
    },
    {
      "bullet_count": 12,
      "heading": "Your day to day",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "In your day to day",
        "last_5_words": "across platforms and products."
      },
      "text": "In your day to day role you will\n\u2022 Work independently or as a team member. Proactively remove obstacles to ensure timely delivery of product and goals \n\u2022 Write clean and solid code that scales over\u202fPB of data and enforce engineering excellence in the organization \n\u2022 Improve data management efficiency through AI capabilities, better process and best practices \n\u2022 Embed Privacy-by-Design principles into all data solutions and ensure compliance with regulatory requirements. \n\u2022 Provide expertise across the data product development lifecycle\u2014spanning data engineering, architecture, and analytics\u2014to design and deliver reusable, accessible, and high-quality data solutions. \n\u2022 Design data structures and taxonomies that support standardization, integration, and alignment with business processes. \n\u2022 Deliver technical leadership through analytical thinking, innovation, and detailed specifications. \n\u2022 Drive data product execution and adoption through a metrics-based approach \n\u2022 Define and communicate data strategy for credit products, aligned with PayPal\u2019s technical direction, evolving credit strategies, and the external data ecosystem \n\u2022 Strong product sense to identify data challenges and opportunities, and assess the impact of data-driven solutions \n\u2022 Leverage enterprise frameworks, governance tools, and reusable architecture patterns for Credit Risk and cross organizations \n\u2022 Foster influential cross-functional relationships through collaboration, proactive planning, and decisive leadership to design scalable solutions across platforms and products.",
      "word_count": 290
    },
    {
      "bullet_count": 6,
      "heading": "Technical Leadership Qualities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Strong collaboration skills and",
        "last_5_words": "transparency and honesty in all interactions"
      },
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}
API 1 — extract-from-jd click to toggle
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        "queue_id": 11134,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Data Governance",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11135,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Enterprise Data Management",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11136,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Streaming",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11137,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Batch Processing",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11138,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Feature Stores",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11140,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Model Monitoring",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11142,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11144,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Privacy-by-Design",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11146,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Data Discovery",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11148,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "Data Quality Monitoring",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 11150,
        "role_display_name": "Data Governance Engineer",
        "role_slug": "data-governance-engineer",
        "skill_name": "ML-assisted ETL",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 300,
      "existing_alias_text": "BigQuery",
      "input_term": "BigQuery",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "BigQuery",
        "id": 106,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 526,
      "existing_alias_text": "Airflow",
      "input_term": "Airflow",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Airflow",
        "id": 265,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "airflow",
        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 173,
      "existing_alias_text": "Kafka",
      "input_term": "Kafka",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "kafka",
        "sub_category_id": 3533,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 405,
      "existing_alias_text": "GCP",
      "input_term": "GCP",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "GCP",
        "id": 186,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "gcp",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2015,
      "existing_alias_text": "Machine Learning",
      "input_term": "Machine Learning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2016,
      "existing_alias_text": "Artificial Intelligence",
      "input_term": "Artificial Intelligence",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Artificial Intelligence",
        "id": 1357,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "artificial-intelligence",
        "sub_category_id": 1020,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
      "input_term": "Data Modeling",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 231,
      "existing_alias_text": "domain-driven design",
      "input_term": "Domain-Driven Design",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "domain-driven design",
        "id": 90,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-driven-design",
        "sub_category_id": 43,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2539,
      "existing_alias_text": "Feature Store",
      "input_term": "Feature Stores",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Feature Store",
        "id": 1593,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "feature-store",
        "sub_category_id": 1199,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "embedding_alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 435,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "fullstack-developer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
    },
    {
      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
      "id": 16,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-ops-engineer",
      "source": "db"
    },
    {
      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Go Backend Developer",
      "id": 81,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
      "id": 79,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    },
    {
      "display_name": "Cloud Architect",
      "id": 9,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Governance Engineer",
    "id": 146,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on enterprise data management, governance, privacy/compliance, standardization, taxonomy, and reusable high-quality data solutions, which best matches a Data Governance Engineer.",
    "role_archetype": null,
    "slug": "data-governance-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Scripting \u0026 DSL Languages",
        "id": 248,
        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
        "slug": "cloud-security-scripting-dsl-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-security-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
        "slug": "programming-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 435,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "fullstack-developer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages and Scripting",
        "id": 59,
        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
        "slug": "programming-languages-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
        "id": 39,
        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
        "slug": "programming-languages-for-ml-systems",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        },
        {
          "display_name": "MLOps Engineer",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for XR",
        "id": 97,
        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
        "slug": "programming-languages-for-xr",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AR/VR Engineer",
          "id": 8,
          "rationale": null,
          "role_archetype": null,
          "slug": "ar-vr-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Python Programming",
        "id": 290,
        "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
        "slug": "python-programming",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Pega Programming Languages \u0026 DSLs",
        "id": 267,
        "rationale": "Programming languages and domain-specific languages used in Pega development.",
        "slug": "pega-programming-languages-dsls",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Pega Developer",
          "id": 24,
          "rationale": null,
          "role_archetype": null,
          "slug": "pega-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Data Warehouses",
        "id": 22,
        "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
        "slug": "cloud-data-warehouses",
        "source": "db"
      },
      "input_skill": "BigQuery",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
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          "slug": "data-engineer",
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    },
    {
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        {
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
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      "roles_from_db": [
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        }
      ]
    },
    {
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        {
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          "display_name": "PHP Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "display_name": "Python Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
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        {
          "display_name": "Ruby Backend Developer",
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      ]
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    {
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        "source": "db"
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        {
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        }
      ]
    },
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        "source": "db"
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      "input_skill": "GCP",
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          "display_name": ".NET Backend Developer",
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        {
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          "rationale": null,
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        {
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          "slug": "data-engineer",
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          "rationale": null,
          "role_archetype": null,
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          "role_archetype": "Engineering",
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          "role_archetype": "Engineering",
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          "slug": "ml-ops-engineer",
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          "rationale": null,
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          "source": "db"
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      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Streaming",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "streaming",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Batch Processing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "batch-processing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Feature Store",
          "alias_type": "CANONICAL",
          "id": 2539,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "Feature Store",
        "id": 1593,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "feature-store",
        "sub_category_id": 1199,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Feature Stores",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Feature Stores",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Model Monitoring",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "model-monitoring",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Privacy-by-Design",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "privacy-by-design",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Discovery",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-discovery",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Quality Monitoring",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-quality-monitoring",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ML-assisted ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "ml-assisted-etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Lambda Architecture",
    "Kappa Architecture",
    "Data Architecture",
    "Data Governance",
    "Enterprise Data Management",
    "Streaming",
    "Batch Processing",
    "Model Monitoring",
    "ETL",
    "Privacy-by-Design",
    "Data Discovery",
    "Data Quality Monitoring",
    "ML-assisted ETL"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Governance Engineer",
    "id": 146,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on enterprise data management, governance, privacy/compliance, standardization, taxonomy, and reusable high-quality data solutions, which best matches a Data Governance Engineer.",
    "role_archetype": null,
    "slug": "data-governance-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "GCP",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Artificial Intelligence",
      "tag": "in_db"
    },
    {
      "skill": "Lambda Architecture",
      "tag": "new"
    },
    {
      "skill": "Kappa Architecture",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Architecture",
      "tag": "new"
    },
    {
      "skill": "Data Governance",
      "tag": "new"
    },
    {
      "skill": "Enterprise Data Management",
      "tag": "new"
    },
    {
      "skill": "Domain-Driven Design",
      "tag": "in_db"
    },
    {
      "skill": "Streaming",
      "tag": "new"
    },
    {
      "skill": "Batch Processing",
      "tag": "new"
    },
    {
      "skill": "Feature Stores",
      "tag": "in_db"
    },
    {
      "skill": "Model Monitoring",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Privacy-by-Design",
      "tag": "new"
    },
    {
      "skill": "Data Discovery",
      "tag": "new"
    },
    {
      "skill": "Data Quality Monitoring",
      "tag": "new"
    },
    {
      "skill": "ML-assisted ETL",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "BigQuery",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 106,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Workflow Orchestration for ML Pipelines",
          "id": 54,
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        "roles_from_db": [
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        "roles_from_db": [
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            "rationale": null,
            "role_archetype": "Engineering",
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            "slug": "scala-backend-developer",
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        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
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      {
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          "difficulty_hint": "well_known",
          "display_name": "Messaging and Background Jobs",
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          "slug": "messaging-and-background-jobs",
          "source": "db"
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        "input_skill": "Kafka",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
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            "display_name": "PHP Backend Developer",
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            "role_archetype": "Engineering",
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        "skipped_reason": null
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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            "slug": "data-engineer",
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        "skill_dimension_saved": true,
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        "skipped_reason": null
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        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms",
          "id": 20,
          "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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          "source": "db"
        },
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        "input_skill": "GCP",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
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          },
          {
            "display_name": "Backend Developer",
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            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
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          },
          {
            "display_name": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
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          },
          {
            "display_name": "Kotlin Backend Developer",
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            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
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          {
            "display_name": "MLOps Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
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          },
          {
            "display_name": "Node.js Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
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          "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
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          "source": "db"
        },
        "dimension_id": 211,
        "input_skill": "GCP",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Platforms",
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          "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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          "source": "db"
        },
        "dimension_id": 131,
        "input_skill": "GCP",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
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            "slug": "cloud-architect",
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          },
          {
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
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          "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
          "slug": "cloud-security-posture-tools",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "GCP",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
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          {
            "display_name": "Cyber Security Engineer",
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            "slug": "cybersecurity-engineer",
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        ],
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        "skipped_reason": null
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      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
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          "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
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        },
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [],
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        "skill_id": 1356,
        "skill_tag": "in_db",
        "skipped_reason": null
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          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
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        },
        "dimension_id": 96,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
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        "skill_id": 1357,
        "skill_tag": "in_db",
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      },
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          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
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        "dimension_id": 293,
        "input_skill": "Data Modeling",
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        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
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            "display_name": ".NET Backend Developer",
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            "rationale": null,
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            "display_name": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
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      {
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        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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        "roles_from_db": [
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            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
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            "rationale": null,
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            "source": "db"
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            "rationale": null,
            "role_archetype": "Engineering",
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            "rationale": null,
            "role_archetype": "Engineering",
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          {
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
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        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 146,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
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        },
        "dimension_id": 293,
        "input_skill": "Domain-Driven Design",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
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            "display_name": "Python Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 90,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 146,
        "dimension": {
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        "roles_from_db": [
          {
            "display_name": "Go Backend Developer",
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            "display_name": "Kotlin Backend Developer",
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            "display_name": "PHP Backend Developer",
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            "rationale": null,
            "role_archetype": "Engineering",
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          },
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            "display_name": "Ruby Backend Developer",
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          },
          {
            "display_name": "Scala Backend Developer",
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            "slug": "scala-backend-developer",
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        ],
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      },
      {
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          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
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        "dimension_id": 96,
        "input_skill": "Feature Stores",
        "llm_role": null,
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        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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        "roles_from_db": [],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
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    ],
    "new_skills_created": 0,
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}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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