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Pipeline run

12c6d66c-7b9c-454b-ad96-9721f5dc6baa

Pipeline LLM cost (USD)
API 1: $0.0096 API 2: $0.0003 API 3: $0.0000 Total: $0.0099

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Own GCP batch pipelines and the financial data warehouse, migrating legacy data systems, modeling schemas, and troubleshooting large-scale workflows so finance data stays accurate, auditable, and timely; also mentor engineers and support data-driven analysis.
""GCP-based batch pipelines (processing millions of financial events per day) are the system of record for revenue, costs, and compliance reporting.""
Tech stack maturity
Modern Cloud Native
The skill set centers on GCP, BigQuery, Apache Airflow, Spark, and Kafka, which are characteristic of modern cloud-native data engineering stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.50 / 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): Machine Learning
Evidence — skills matched in JD (21)
GCP Dataflow Dataproc Apache Spark Kafka BigQuery Cloud SQL MySQL PostgreSQL Oracle Apache Airflow ETL Data Modeling Schema Design Batch Pipelines Informatica PowerCenter Tableau Indexing Machine Learning Clustering Distributed Computing
Skill cluster (12 dimension groups, role-scoped)
Relational Database Usage
MySQL PostgreSQL
AI Governance and Model Security
Machine Learning
BI and Visualization Tools
Tableau
Cloud Data Warehouses
BigQuery
Cloud Platforms
GCP
Data Pipeline Orchestration
Apache Airflow
ETL and ELT Tooling
Apache Spark
Messaging and Event Streaming
Kafka
Performance and Cost Optimization
Clustering
Relational Data Modeling
Schema Design
Relational Database Design
Indexing
Cross-cutting / unaligned
Dataflow Dataproc Cloud SQL Oracle ETL Data Modeling Batch Pipelines Informatica PowerCenter Distributed Computing
Show KRA description ↓
As a Senior Data Engineer in our FinTech data engineering team, you’ll own the cloud-scale data backbone that powers finance, accounting, and ERP across the company. Our financial data warehouse and GCP-based batch pipelines (processing millions of financial events per day) are the system of record for revenue, costs, and compliance reporting. You will be designing and hardening the platforms that ensure every transaction is accurate, auditable, and timely—directly influencing decisions made by finance leaders and executives while mentoring other engineers and shaping our long-term data architecture on GCP. • Participate in cloud migration and modernizing current data systems. • Oversee data systems that serve millions of events a day. • Maintain and design early warning and analytics systems using cutting edge big data technologies. • Collaborate on building software that collects and queries data, while also composing queries for investigation purposes. • Analyze and provide data supported recommendations to improve product performance and customer acquisition. • Diagnose and troubleshoot any issues within data infrastructure. • Effectively collaborate and engage in team efforts, mentoring the junior members in the team, speak up for what you think are the best solutions and be able to converse respectfully and compromise when necessary. • Minimum of 5+ years of experience in software engineering and development, with a strong focus on Big Data technologies including frameworks such as Apache Spark and Kafka. Proficiency in the Google Cloud Platform (GCP) - Dataflow and Dataproc, for building and managing large-scale data processing pipelines. • Advanced knowledge in data modeling and schema design for optimized data storage and retrieval. • Strong command of databases, including CloudSQL (MySQL, PostgreSQL), BigQuery, and Oracle. • Proficiency in data orchestration frameworks, particularly Apache Airflow, for scheduling and managing complex workflows. • Proven ability to analyze, debug, and resolve data processing issues efficiently. • Experience with enterprise ETL tools such as Informatica (PowerCenter or similar) is a plus, especially for understanding and helping migrate legacy batch processes into GCP-based pipelines. • Experience with data visualization tools, such as Tableau, is a plus for representing data insights. • Familiarity with advanced data processing strategies, including indexing, machine-learned ranking algorithms, clustering techniques, and distributed computing concepts. (advance concepts of Spark). • Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust. • The Right Results, the Right Way is not just a motto at Priceline; it’s a way of life. Unquestionable integrity and ethics is essential.

Signals

Skill node-backend-developer
0.33
Alias data-engineer
1.00
KRA data-engineer
0.58

Post-classification

Centroidupdated · n=441
Alias collision log
New-role queue
New skills captured9
New KRA captured

Captured for admin review

Dataflow primary Data Engineer pending
Dataproc primary Data Engineer pending
Cloud SQL primary Data Engineer pending
Oracle primary Data Engineer pending
Informatica PowerCenter Data Engineer pending
ETL primary Data Engineer pending
Data Modeling primary Data Engineer pending
Distributed Computing Data Engineer pending
Batch Pipelines primary Data Engineer pending
Status: completed Created: 2026-05-27T16:29:52.332368Z Updated: 2026-05-27T16:32:10.112255Z API 3 duration: 70452 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 Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: data-engineer · id: 2 · source: db

Domain=Data Engineering & Analytics; The JD centers on cloud-scale data pipelines, Big Data processing, data infrastructure, migration, orchestration, and warehouse engineering on GCP, which best matches Data Engineer.

Matched skills

GCPApache SparkKafkaDataflowDataprocBigQueryCloudSQLOracleApache AirflowInformaticaTableaumachine-learned ranking algorithmsclustering

Matched dimensions

Cloud Data EngineeringBig Data Pipeline DevelopmentData Warehouse EngineeringData Migration and ModernizationWorkflow OrchestrationData Modeling and Schema DesignData Infrastructure ReliabilityTechnical Mentoring and Collaboration

Matched KRAs

own the cloud-scale data backboneparticipate in cloud migration and modernizing current data systemsoversee data systems that serve millions of events a daymaintain and design early warning and analytics systemscollects and queries datacompose queries for investigation purposesanalyze and provide data supported recommendationsdiagnose and troubleshoot any issues within data infrastructurementor the junior members in the teamdesigning and hardening the platforms

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
5
Skipped

Job description

This role is eligible for our hybrid work model: Two days in-office.

Senior Data Engineer

Our Technology team is the backbone of our company: constantly creating, testing, learning and iterating to better meet the needs of our customers. If you thrive in a fast-paced, ideas-led environment, you’re in the right place.

Why This Job’s a Big Deal

As a Senior Data Engineer in our FinTech data engineering team, you’ll own the cloud-scale data backbone that powers finance, accounting, and ERP across the company. Our financial data warehouse and GCP-based batch pipelines (processing millions of financial events per day) are the system of record for revenue, costs, and compliance reporting. You will be designing and hardening the platforms that ensure every transaction is accurate, auditable, and timely—directly influencing decisions made by finance leaders and executives while mentoring other engineers and shaping our long-term data architecture on GCP.

In This Role You Will Get To

• Participate in cloud migration and modernizing current data systems.
• Oversee data systems that serve millions of events a day.
• Maintain and design early warning and analytics systems using cutting edge big data technologies.
• Collaborate on building software that collects and queries data, while also composing queries for investigation purposes.
• Analyze and provide data supported recommendations to improve product performance and customer acquisition. 
• Diagnose and troubleshoot any issues within data infrastructure.
• Effectively collaborate and engage in team efforts, mentoring the junior members in the team, speak up for what you think are the best solutions and be able to converse respectfully and compromise when necessary.


Who You Are

• Bachelor’s degree in Computer Science or a related field.
• Minimum of 5+ years of experience in software engineering and development, with a strong focus on Big Data technologies including frameworks such as Apache Spark and Kafka. Proficiency in the Google Cloud Platform (GCP) - Dataflow and Dataproc, for building and managing large-scale data processing pipelines.
• Advanced knowledge in data modeling and schema design for optimized data storage and retrieval.
• Strong command of databases, including CloudSQL (MySQL, PostgreSQL), BigQuery, and Oracle.
• Proficiency in data orchestration frameworks, particularly Apache Airflow, for scheduling and managing complex workflows.
• Proven ability to analyze, debug, and resolve data processing issues efficiently.
• Experience with enterprise ETL tools such as Informatica (PowerCenter or similar) is a plus, especially for understanding and helping migrate legacy batch processes into GCP-based pipelines.
• Experience with data visualization tools, such as Tableau, is a plus for representing data insights.
• Familiarity with advanced data processing strategies, including indexing, machine-learned ranking algorithms, clustering techniques, and distributed computing concepts. (advance concepts of Spark).
• Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust. 
• The Right Results, the Right Way is not just a motto at Priceline; it’s a way of life. Unquestionable integrity and ethics is essential.


Who We Are

WE ARE PRICELINE.

Our success as one of the biggest players in online travel is all thanks to our incredible, dedicated team of talented employees. Priceliners are focused on being the best travel deal makers in the world, motivated by our passion to help everyone experience the moments that matter most in their lives. Whether it’s a dream vacation, your cousin’s graduation, or your best friend’s wedding - we make travel affordable and accessible to our customers.

Our culture is unique and inspiring (that’s what our employees tell us). We’re a grown-up, startup. We deliver the excitement of a new venture, without the struggles and chaos that can come with a business that hasn’t stabilized.

We’re on the cutting edge of innovative technologies. We keep the customer at the center of all that we do. Our ability to meet their needs relies on the strength of a workforce as diverse as the customers we serve. We bring together employees from all walks of life and we are proud to provide the kind of inclusive environment that stimulates innovation, creativity and collaboration.

Priceline is part of the Booking Holdings, Inc. (Nasdaq: BKNG) family of companies, a highly profitable global online travel company with a market capitalization of over $80 billion. Our sister companies include Booking.com, BookingGo, Agoda, Kayak and OpenTable.

If you want to be part of something truly special, check us out!

Flexible work at Priceline

Priceline is following a hybrid working model, which includes two days onsite as determined by you and your manager (ideally selecting among Tuesday, Wednesday, or Thursday). On the remaining days, you can choose to be remote or in the office.

Inclusion is a Big Deal !

To be the best travel dealmakers in the world, we believe our team should reflect the broad range of customers and communities we serve. We are committed to cultivating a culture where all employees have the freedom to bring their individual perspectives, life experiences, and passion to work.

Priceline is a proud equal opportunity employer. We embrace and celebrate the unique lenses through which our employees see the world. We’d love for you to join us and help shape what makes our team extraordinary.

Applying for this position

We're excited that you are interested in a career with us. For all current employees, please use the internal portal to find jobs and apply.

External candidates are required to have an account before applying. When you click Apply, returning candidates can log in, or new candidates can quickly create an account to save/view applications.

Skills from this JD

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

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 saved
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)
Dataflow 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
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Dataproc 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache 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 saved
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 saved
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 saved
Cloud SQL 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
MySQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MySQL id=17 · mysql

Aliases — catalog

  • MySQL (CANONICAL) primary

Context tags (catalog)

ACID ACID compliance Backup Data Warehousing Database Design ER Diagrams EXPLAIN Indexes InnoDB JOIN Joins MyISAM Normalization PHPMyAdmin Query Optimization Replication SQL SQL queries Stored Procedures Transactions Triggers backup backup and restore backup strategies data modeling data normalization database design database normalization database schema foreign keys indexes indexing joins master-slave normalization performance tuning query optimization read replicas replication schema design schema management sharding stored procedures transaction isolation transaction management transactions triggers

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Oracle Corporation
License
gpl_v2
Year introduced
1995
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: MySQL appears in a large share of backend/DB job descriptions and remains a standard managed offering across AWS RDS, Cloud SQL, and Azure Database, indicating broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Oracle 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
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache Airflow Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Airflow id=110 · apache-airflow

Aliases — catalog

  • Apache Airflow (CANONICAL) primary

Context tags (catalog)

CeleryExecutor DAG ETL KubernetesExecutor Sensors XCom backfill catchup cron data pipelines executor hooks operators scheduler task dependencies

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2015
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Frequently listed in data engineering JDs and widely adopted for workflow orchestration; strong GitHub activity and managed offerings from AWS/GCP/Azure signal broad market demand.

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)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension saved
Informatica PowerCenter 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Tableau Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Tableau id=150 · tableau

Aliases — catalog

  • Tableau (CANONICAL) primary

Context tags (catalog)

LOD expressions Tableau Cloud Tableau Desktop Tableau Prep Tableau Server actions calculated fields dashboards data blending data visualization extracts filters parameters published data sources workbooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Tableau Software
License
proprietary
Year introduced
2003
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
ETL 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
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
Schema Design Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: schema design id=19 · schema-design

Aliases — catalog

  • schema design (CANONICAL) primary
  • Schema Design (CANONICAL)

Context tags (catalog)

ACID DDL ER modeling ERD NoSQL SQL cardinality constraints data governance data integrity data lineage data model data modeling data normalization data types data warehouse database design database normalization database optimization database schema denormalization dimensional modeling entity relationship entity-relationship entity-relationship diagram foreign key foreign keys indexes indexing metadata management normalization primary key primary keys query optimization relational relational databases relational model relational schema schema evolution schema migration snowflake schema star schema

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Schema Design
Confidence
0.84
Version strategy
NOT_APPLICABLE

Maturity reasoning: Schema design is a standard requirement in backend/data JDs and appears across SQL, data engineering, and SaaS roles; it’s a core interview topic rather than a niche specialty.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2479
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexing Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: indexing id=20 · indexing

Aliases — catalog

  • indexing (CANONICAL) primary

Context tags (catalog)

B-tree Elasticsearch Lucene NoSQL NoSQL indexing SQL indexing bitmap index bitmap indexes cardinality clustered index columnstore index composite index composite indexes covering index data retrieval data warehousing database optimization database performance denormalization execution plan foreign key full-text search hash indexing index fragmentation index maintenance index scan index seek index structure indexing overhead indexing strategies indexing strategy inverted index materialized view non-clustered index nonclustered index normalization partitioned index partitioning primary key query optimization query optimizer query performance search algorithms secondary index secondary indexes selectivity unique index

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Indexing
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Database indexing is a standard requirement in SQL/NoSQL job descriptions and core DB docs; it’s broadly expected for performance tuning rather than a niche specialty.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2477
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning Secondary 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)
Clustering Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: clustering id=162 · clustering

Aliases — catalog

  • clustering (CANONICAL) primary
  • Clustering (CANONICAL)

Context tags (catalog)

DBSCAN Gaussian mixture Gaussian mixture model PCA agglomerative centroid cluster analysis clustering algorithms data partitioning dendrogram density-based dimensionality reduction distance metric elbow method feature extraction feature scaling hierarchical hierarchical clustering k-means outlier detection silhouette score spectral clustering unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems Concept
Confidence
0.72
Version strategy
NOT_APPLICABLE

Maturity reasoning: Clustering is a standard distributed-systems concept and appears broadly in JDs for databases, Kubernetes, and load-balanced services; vendor docs for AWS, Kubernetes, and PostgreSQL all treat clustering as a common production pattern.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1053
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Concurrency and Parallel Processing Catalog dimension db id 17

    Library dimension (catalog)

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

  • Performance and Cost Optimization Catalog dimension db id 33

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Concurrency and Parallel Processing
concurrency-and-parallel-processing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Performance and Cost Optimization
performance-and-cost-optimization
Existing dimension (library) · Role↔dimension saved
Distributed Computing Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Distributed Systems id=1369 · distributed-systems

Aliases — catalog

  • Distributed Systems (CANONICAL)

Context tags (catalog)

CAP theorem Docker Swarm Kafka MapReduce Zookeeper consensus algorithms distributed databases eventual consistency fault tolerance gRPC load balancing message queues microservices replication sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1035
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

  • Performance and Scalability Tuning Catalog dimension db id 11

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Performance and Scalability Tuning
performance-and-scalability-tuning
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Batch Pipelines 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

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
GCP in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
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)
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
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 saved
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
MySQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Airflow in_db
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension saved
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
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
Schema Design in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Schema Design in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexing in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexing in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Indexing in_db
Search and Content Discovery
search-and-content-discovery
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)
Clustering in_db
Concurrency and Parallel Processing
concurrency-and-parallel-processing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Clustering in_db
Performance and Cost Optimization
performance-and-cost-optimization
Existing dimension (library) · Role↔dimension saved
Distributed Computing new
Cloud Platforms
cloud-platforms
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Distributed Computing new
Performance and Scalability Tuning
performance-and-scalability-tuning
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Distributed Computing 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 Dataflow | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Dataproc | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Cloud SQL | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Informatica PowerCenter | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Batch Pipelines | 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 Distributed Computing ↔ Cloud Platforms
role_dimension_link_proposed Data Engineer ↔ Cloud Platforms
dimension_skill_link_proposed Distributed Computing ↔ Performance and Scalability Tuning
dimension_skill_link_proposed Distributed Computing ↔ React Frontend Development
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior Data Engineer
CompanyPriceline
ExperienceMinimum of 5+ years of experience in software engineering and development
DomainTravel & Hospitality
Location(hybrid)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Our success as one of",
      "last_5_words": "affordable and accessible to our customers."
    },
    "text": "Our success as one of the biggest players in online travel is all thanks to our incredible, dedicated team of talented employees. Priceliners are focused on being the best travel deal makers in the world, motivated by our passion to help everyone experience the moments that matter most in their lives. Whether it\u2019s a dream vacation, your cousin\u2019s graduation, or your best friend\u2019s wedding - we make travel affordable and accessible to our customers.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "Priceline",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Online Travel",
        "Travel Tech"
      ],
      "domain": "Travel \u0026 Hospitality"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science (or related)",
      "raw": "Bachelor\u2019s degree in Computer Science or a related field.",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "Minimum of 5+ years of experience in software engineering and development"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": null,
      "state": null,
      "work_mode": "hybrid"
    }
  ],
  "role": "Senior Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Big Data Engineer",
    "Senior Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Why This Job\u2019s a Big Deal",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "As a Senior Data Engineer",
        "last_5_words": "data architecture on GCP."
      },
      "text": "As a Senior Data Engineer in our FinTech data engineering team, you\u2019ll own the cloud-scale data backbone that powers finance, accounting, and ERP across the company. Our financial data warehouse and GCP-based batch pipelines (processing millions of financial events per day) are the system of record for revenue, costs, and compliance reporting. You will be designing and hardening the platforms that ensure every transaction is accurate, auditable, and timely\u2014directly influencing decisions made by finance leaders and executives while mentoring other engineers and shaping our long-term data architecture on GCP.",
      "word_count": 83
    },
    {
      "bullet_count": 7,
      "heading": "In This Role You Will Get To",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Participate in cloud migration",
        "last_5_words": "respectfully and compromise when necessary."
      },
      "text": "\u2022 Participate in cloud migration and modernizing current data systems.\n\u2022 Oversee data systems that serve millions of events a day.\n\u2022 Maintain and design early warning and analytics systems using cutting edge big data technologies.\n\u2022 Collaborate on building software that collects and queries data, while also composing queries for investigation purposes.\n\u2022 Analyze and provide data supported recommendations to improve product performance and customer acquisition.\n\u2022 Diagnose and troubleshoot any issues within data infrastructure.\n\u2022 Effectively collaborate and engage in team efforts, mentoring the junior members in the team, speak up for what you think are the best solutions and be able to converse respectfully and compromise when necessary.",
      "word_count": 104
    },
    {
      "bullet_count": 10,
      "heading": "Who You Are",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Minimum of 5+ years of",
        "last_5_words": "integrity and ethics is essential."
      },
      "text": "\u2022 Minimum of 5+ years of experience in software engineering and development, with a strong focus on Big Data technologies including frameworks such as Apache Spark and Kafka. Proficiency in the Google Cloud Platform (GCP) - Dataflow and Dataproc, for building and managing large-scale data processing pipelines.\n\u2022 Advanced knowledge in data modeling and schema design for optimized data storage and retrieval.\n\u2022 Strong command of databases, including CloudSQL (MySQL, PostgreSQL), BigQuery, and Oracle.\n\u2022 Proficiency in data orchestration frameworks, particularly Apache Airflow, for scheduling and managing complex workflows.\n\u2022 Proven ability to analyze, debug, and resolve data processing issues efficiently.\n\u2022 Experience with enterprise ETL tools such as Informatica (PowerCenter or similar) is a plus, especially for understanding and helping migrate legacy batch processes into GCP-based pipelines.\n\u2022 Experience with data visualization tools, such as Tableau, is a plus for representing data insights.\n\u2022 Familiarity with advanced data processing strategies, including indexing, machine-learned ranking algorithms, clustering techniques, and distributed computing concepts. (advance concepts of Spark).\n\u2022 Illustrated history of living the values necessary to Priceline: Customer, Innovation, Team, Accountability and Trust.\n\u2022 The Right Results, the Right Way is not just a motto at Priceline; it\u2019s a way of life. Unquestionable integrity and ethics is essential.",
      "word_count": 218
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "GCP"
    },
    {
      "is_primary": true,
      "skill_name": "Dataflow"
    },
    {
      "is_primary": true,
      "skill_name": "Dataproc"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "BigQuery"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud SQL"
    },
    {
      "is_primary": true,
      "skill_name": "MySQL"
    },
    {
      "is_primary": true,
      "skill_name": "PostgreSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Airflow"
    },
    {
      "is_primary": false,
      "skill_name": "Informatica PowerCenter"
    },
    {
      "is_primary": false,
      "skill_name": "Tableau"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Schema Design"
    },
    {
      "is_primary": false,
      "skill_name": "Indexing"
    },
    {
      "is_primary": false,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": false,
      "skill_name": "Clustering"
    },
    {
      "is_primary": false,
      "skill_name": "Distributed Computing"
    },
    {
      "is_primary": true,
      "skill_name": "Batch Pipelines"
    }
  ],
  "jd_role": {
    "display_name": "Senior Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Big Data Engineer",
      "Senior Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Our success as one of",
        "last_5_words": "affordable and accessible to our customers."
      },
      "text": "Our success as one of the biggest players in online travel is all thanks to our incredible, dedicated team of talented employees. Priceliners are focused on being the best travel deal makers in the world, motivated by our passion to help everyone experience the moments that matter most in their lives. Whether it\u2019s a dream vacation, your cousin\u2019s graduation, or your best friend\u2019s wedding - we make travel affordable and accessible to our customers.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "Priceline",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Online Travel",
          "Travel Tech"
        ],
        "domain": "Travel \u0026 Hospitality"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE - Computer Science (or related)",
        "raw": "Bachelor\u2019s degree in Computer Science or a related field.",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 5,
      "raw": "Minimum of 5+ years of experience in software engineering and development"
    },
    "job_locations": [
      {
        "aliases": [],
        "city": null,
        "country": null,
        "state": null,
        "work_mode": "hybrid"
      }
    ],
    "role": "Senior Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Big Data Engineer",
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API 2 — extract-details
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            "id": 216,
            "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
            "slug": "relational-data-modeling",
            "source": "db"
          },
          "input_skill": "Schema Design",
          "llm_role": null,
          "roles_from_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": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "Schema Design",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "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": "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": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Schema Design",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "indexing",
          "alias_type": "CANONICAL",
          "id": 148,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "indexing",
        "id": 20,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "indexing",
        "sub_category_id": 2477,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Data Modeling",
            "id": 216,
            "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
            "slug": "relational-data-modeling",
            "source": "db"
          },
          "input_skill": "Indexing",
          "llm_role": null,
          "roles_from_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": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "Indexing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "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": "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": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Search and Content Discovery",
            "id": 356,
            "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
            "slug": "search-and-content-discovery",
            "source": "db"
          },
          "input_skill": "Indexing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Drupal Dev",
              "id": 228,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "drupal-dev",
              "source": "db"
            },
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Indexing",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 2015,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "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"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "AI Governance and Model Security",
            "id": 50,
            "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.",
            "slug": "ai-governance-and-model-security",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-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"
            }
          ]
        },
        {
          "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": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "clustering",
          "alias_type": "CANONICAL",
          "id": 3841,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Clustering",
          "alias_type": "CANONICAL",
          "id": 371,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "clustering",
        "id": 162,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "clustering",
        "sub_category_id": 1053,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Concurrency and Parallel Processing",
            "id": 17,
            "rationale": "Programming techniques for handling multiple requests and background work safely and efficiently. Includes synchronization, async execution, and coordination of concurrent tasks.",
            "slug": "concurrency-and-parallel-processing",
            "source": "db"
          },
          "input_skill": "Clustering",
          "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": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-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": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Performance and Cost Optimization",
            "id": 33,
            "rationale": "Techniques for improving the speed, reliability, and cost efficiency of data workloads. This includes query tuning, partitioning, file sizing, compute right-sizing, and workload management.",
            "slug": "performance-and-cost-optimization",
            "source": "db"
          },
          "input_skill": "Clustering",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Clustering",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Distributed Systems",
          "alias_type": "CANONICAL",
          "id": 2028,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Distributed Systems",
        "id": 1369,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "distributed-systems",
        "sub_category_id": 1035,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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.",
            "slug": "cloud-platforms",
            "source": "db"
          },
          "input_skill": "Distributed Computing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "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": "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": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-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": "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": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Performance and Scalability Tuning",
            "id": 11,
            "rationale": "Techniques for improving throughput, latency, and resource efficiency in PHP backend services. This includes profiling, query optimization, concurrency limits, memory use, and bottleneck analysis.",
            "slug": "performance-and-scalability-tuning",
            "source": "db"
          },
          "input_skill": "Distributed Computing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "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": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "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": "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": "Distributed Computing",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Distributed Computing",
      "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": "Batch Pipelines",
      "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": "batch-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Dataflow",
    "Dataproc",
    "Cloud SQL",
    "Oracle",
    "Informatica PowerCenter",
    "ETL",
    "Batch Pipelines"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on cloud-scale data pipelines, Big Data processing, data infrastructure, migration, orchestration, and warehouse engineering on GCP, which best matches Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "GCP",
      "tag": "in_db"
    },
    {
      "skill": "Dataflow",
      "tag": "new"
    },
    {
      "skill": "Dataproc",
      "tag": "new"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "Cloud SQL",
      "tag": "new"
    },
    {
      "skill": "MySQL",
      "tag": "in_db"
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