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Pipeline run

3b6789e7-bcb7-4872-9e38-a7b681daafa5

Pipeline LLM cost (USD)
API 1: $0.0040 API 2: $0.0003 API 3: $0.0000 Total: $0.0043

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
Build and tune large-scale Python/SQL analytics workflows and data pipelines in Pandas/Dask/PySpark, including batch/streaming processing, API integrations, and distributed systems, while working with domain experts to turn analytics goals into mathematical methods for diagnostic, predictive, and prescriptive insights.
"developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics"
Tech stack maturity
Modern Cloud Native
The stack centers on Python services, event-driven architecture, Airflow, Kafka, Flink, and Spark streaming, which strongly aligns with modern cloud-native data engineering practices.
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): Machine Learning
Evidence — skills matched in JD (21)
Python Pandas Dask PySpark SQL NoSQL Airflow Perfect Flask Django FastAPI Distributed Computing Big Data Event-Driven Architecture Real-Time Processing Apache Kafka Apache Flink Spark Streaming Machine Learning Statistics Analytics
Skill cluster (6 dimension groups, role-scoped)
Web Application Frameworks
Flask Django FastAPI
Programming Languages for Data Work
Python SQL
Stream Processing Systems
Apache Flink Spark Streaming
AI Governance and Model Security
Machine Learning
Messaging and Event Streaming
Apache Kafka
Cross-cutting / unaligned
Pandas Dask PySpark NoSQL Airflow Perfect Distributed Computing Big Data Event-Driven Architecture Real-Time Processing Statistics Analytics
Show KRA description ↓
You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics. The automated workflows must be tuned for large-scale data processing in a performant and scalable manner. The ideal candidate must be an independent problem solver with excellent communication skills. Your ability to stay up to date with the changes in the Big Data Analytics and Machine Learning technology landscape is critical for your success in the team. This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight. Candidate must know the underlying mathematical foundations of statistics, machine learning, and analytics. The ability to transform a data analytics objective into a mathematical process is must must-have skill for this role. • Proficiency in scripting languages such as Python. • Strong command of libraries such as Pandas, Dask, and PySpark. • Deep understanding of SQL, NoSQL, and relational database design, and proficiency in using SQL to extract and analyze data. • Experience with data pipeline and workflow management tools such as Airflow, Perfect. • Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy. • The ideal candidate should be familiar with working with API servers, including Flask, Django, or FastAPI. • Candidates should have demonstrable experience in distributed computing and managing big data systems. • They must be self-motivated and stay updated with the latest trends and advancements in technology. • Understanding of event-driven architectures and real-time processing. • Knowledge of streaming technologies like Apache Kafka, Apache Flink, and Spark streaming.

Signals

Skill data-engineer
0.29
Alias data-engineer
1.00
KRA data-engineer
0.54

Post-classification

Centroidupdated · n=238
Alias collision log
New-role queue
New skills captured8
New KRA captured

Captured for admin review

Pandas primary Data Engineer pending
Dask primary Data Engineer pending
PySpark primary Data Engineer pending
Perfect primary Data Engineer pending
Distributed Computing primary Data Engineer pending
Big Data primary Data Engineer pending
Real-Time Processing primary Data Engineer pending
Statistics primary Data Engineer pending
Status: completed Created: 2026-05-27T14:58:11.705067Z Updated: 2026-06-12T16:59:46.391233Z API 3 duration: 10953 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

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.29 does not contradict

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
4
Skipped

Job description

You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics. The automated workflows must be tuned for large-scale data processing in a performant and scalable manner.

The ideal candidate must be an independent problem solver with excellent communication skills. Your ability to stay up to date with the changes in the Big Data Analytics and Machine Learning technology landscape is critical for your success in the team.

This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight.

Candidate must know the underlying mathematical foundations of statistics, machine learning, and analytics. The ability to transform a data analytics objective into a mathematical process is must must-have skill for this role.

• Proficiency in scripting languages such as Python.
• Strong command of libraries such as Pandas, Dask, and PySpark.
• Deep understanding of SQL, NoSQL, and relational database design, and proficiency in using SQL to extract and analyze data.
• Experience with data pipeline and workflow management tools such as Airflow, Perfect.
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• The ideal candidate should be familiar with working with API servers, including Flask, Django, or FastAPI.
• Candidates should have demonstrable experience in distributed computing and managing big data systems.
• They must be self-motivated and stay updated with the latest trends and advancements in technology.
• Understanding of event-driven architectures and real-time processing.
• Knowledge of streaming technologies like Apache Kafka, Apache Flink, and Spark streaming.

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 saved
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)
Pandas 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
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Dask 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
PySpark 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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 saved
NoSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=1346 · nosql

Aliases — catalog

  • NoSQL (CANONICAL)

Context tags (catalog)

CAP theorem Cassandra DynamoDB MongoDB Redis column-family data modeling document store eventual consistency graph database horizontal scaling key-value store query language schema-less sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Paradigm
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1019
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
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)
Perfect 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
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Flask Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Flask id=1344 · flask

Aliases — catalog

  • Flask (CANONICAL) primary
  • flask 2 (VERSION)
  • flask 2.x (VERSION)
  • flask 3 (VERSION)
  • flask 3.x (VERSION)
  • flask2 (VERSION)
  • flask3 (VERSION)
  • flask>=3 (VERSION)

Context tags (catalog)

API Blueprints Flask-Migrate Flask-RESTful Flask-SQLAlchemy Flask-WTF JSON Jinja2 RESTful RESTful APIs SQLAlchemy Werkzeug debugging deployment gunicorn middleware routing session management template rendering unit testing virtual environments virtualenv

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Pallets Projects
License
bsd
Year introduced
2010
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Flask appears in many Python web developer job postings and remains a common lightweight framework in hiring pipelines, though often alongside Django/FastAPI rather than as a niche tool.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

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

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)
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Django Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Django id=9 · django

Aliases — catalog

  • Django (CANONICAL) primary
  • Django 1 (VERSION)
  • Django 1.x (VERSION)
  • Django 2 (VERSION)
  • Django 2.x (VERSION)
  • Django 3 (VERSION)
  • Django 3.x (VERSION)
  • Django 4 (VERSION)
  • Django 4.x (VERSION)
  • Django 5 (VERSION)
  • Django 5.x (VERSION)
  • Django1 (VERSION)
  • Django2 (VERSION)
  • Django3 (VERSION)
  • Django4 (VERSION)
  • Django5 (VERSION)
  • django 2 (VERSION)
  • django 2.x (VERSION)
  • django 3 (VERSION)
  • django 3.x (VERSION)
  • django 4 (VERSION)
  • django 4.x (VERSION)
  • django 5 (VERSION)
  • django 5.0 (VERSION)
  • django 5.x (VERSION)
  • django2 (VERSION)
  • django2.x (VERSION)
  • django3 (VERSION)
  • django3.x (VERSION)
  • django4 (VERSION)
  • django4.x (VERSION)
  • django5 (VERSION)
  • django5.0 (VERSION)
  • django5.x (VERSION)

Context tags (catalog)

Celery Django REST Framework Django Signals Jinja2 MVT ORM PostgreSQL QuerySet REST URL routing admin interface admin site authentication celery csrf deployment forms gunicorn middleware migrations models pytest querysets settings signals static files templates views

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Django Software Foundation
License
bsd
Year introduced
2005
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
5

Maturity reasoning: Django appears in many backend web job descriptions and remains a standard Python web framework; its GitHub ecosystem and long-term LTS releases show sustained market demand.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: FastAPI id=1201 · fastapi

Aliases — catalog

  • FastAPI (CANONICAL) primary

Context tags (catalog)

API documentation ASGI CORS JSON JSON Schema OAuth2 OpenAPI Pydantic RESTful Starlette UVicorn WebSocket async async programming data validation dependency injection middleware path parameters query parameters type hints uvicorn

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Sebastián Ramírez
License
mit
Year introduced
2018
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

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

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)
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Computing Primary 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
Big Data 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Event-Driven Architecture Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Event-Driven Architecture id=1360 · event-driven-architecture

Aliases — catalog

  • Event-Driven Architecture (CANONICAL)

Context tags (catalog)

CQRS Kafka RabbitMQ asynchronous messaging data pipeline event bus event schema event sourcing event-driven programming message broker microservices publish-subscribe real-time data serverless stream processing

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Event Driven Architecture
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in cloud-native JDs and vendor docs; AWS, Azure, and Confluent all market event-driven patterns with Kafka/PubSub, showing broad hiring demand.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1027
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)
Real-Time 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Kafka id=145 · apache-kafka

Aliases — catalog

  • Apache Kafka (CANONICAL) primary

Context tags (catalog)

Avro Kafka Streams Schema Registry ZooKeeper brokers consumer group event streaming exactly-once semantics ksqlDB message queue offsets partitioning pub/sub replication topics

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Event Streaming Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Apache Kafka is broadly adopted in production and appears frequently in job descriptions for event streaming, data pipelines, and microservices; it remains a common hiring-pipeline staple across backend and platform roles.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
128
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • 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
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Apache Flink Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Flink id=120 · apache-flink

Aliases — catalog

  • Apache Flink (CANONICAL) primary
  • Apache Flink 1.20 (VERSION)
  • Apache Flink 1.x (VERSION)
  • Flink 1.20 (VERSION)
  • Flink 1.x (VERSION)

Context tags (catalog)

CEP DataStream API Flink SQL Kafka Kinesis SQL Table API checkpointing event time exactly-once state backend stateful processing stream processing watermarks windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
1.20

Maturity reasoning: Apache Flink appears in streaming/data-platform JDs, but far less often than Spark/Kafka; GitHub and job-market signals show a specialized real-time processing niche rather than broad hiring staple.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
94
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Stream Processing Systems Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
Spark Streaming Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Spark Streaming id=121 · spark-streaming

Aliases — catalog

  • DStreams (VERSION)
  • Spark 2.x (VERSION)
  • Spark 3.x (VERSION)
  • Spark Streaming (VERSION)
  • Spark Structured Streaming (VERSION)
  • Structured Streaming (VERSION)

Context tags (catalog)

DStreams Kafka Kinesis Structured Streaming backpressure checkpointing event time exactly-once micro-batch stateful processing streaming ETL trigger intervals watermarking window functions windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2013
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
Structured Streaming (Spark 2.0+)

Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
94
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Stream Processing Systems Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
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)
Statistics 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Analytics Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Analytics id=1664 · analytics

Aliases — catalog

  • Analytics (CANONICAL)

Context tags (catalog)

A/B testing ETL KPI Python R SQL business intelligence dashboards data mining data storytelling data visualization data warehousing machine learning predictive modeling statistical analysis

Stored enrichment (catalog DB)

Category
Domain
Sub-category
Analytics
Confidence
0.94
Version strategy
NOT_APPLICABLE

Maturity reasoning: Analytics appears in a large share of data, product, and BI job descriptions, and major vendors (Google Analytics, Adobe Analytics, Power BI) continue to invest heavily in the category.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
37
Sub-category id
1257
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)

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 saved
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)
PySpark new
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
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 saved
NoSQL in_db
NoSQL Databases
nosql-databases
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)
Flask in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flask in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Django in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
Event-Driven Architecture in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Apache Flink in_db
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
Spark Streaming in_db
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
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)
Analytics in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed Dask | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Perfect | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Big Data | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Real-Time Processing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Statistics | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
dimension_skill_link_proposed PySpark ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
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
RoleBig Data Analytics Developer
DomainIT Services & Consulting
JD type pass
Show raw JSON
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  "about_company": null,
  "certifications": [],
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    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
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  },
  "job_locations": [],
  "role": "Big Data Analytics Developer",
  "role_aliases": [
    "Data Engineer",
    "Big Data Engineer",
    "Analytics Developer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
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      "bullet_count": 0,
      "heading": "Role Overview",
      "heading_was_present": false,
      "source_marker": {
        "first_5_words": "You will be responsible for",
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      },
      "text": "You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics. The automated workflows must be tuned for large-scale data processing in a performant and scalable manner.",
      "word_count": 32
    },
    {
      "bullet_count": 0,
      "heading": "Ideal Candidate",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "The ideal candidate must be",
        "last_5_words": "success in the team."
      },
      "text": "The ideal candidate must be an independent problem solver with excellent communication skills. Your ability to stay up to date with the changes in the Big Data Analytics and Machine Learning technology landscape is critical for your success in the team.",
      "word_count": 40
    },
    {
      "bullet_count": 0,
      "heading": "Collaboration",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "This role involves collaborating with",
        "last_5_words": "efficient in extracting the insight."
      },
      "text": "This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight.",
      "word_count": 28
    },
    {
      "bullet_count": 0,
      "heading": "Must-have Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Candidate must know the underlying",
        "last_5_words": "must-have skill for this role."
      },
      "text": "Candidate must know the underlying mathematical foundations of statistics, machine learning, and analytics. The ability to transform a data analytics objective into a mathematical process is must must-have skill for this role.",
      "word_count": 38
    },
    {
      "bullet_count": 10,
      "heading": "Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Proficiency in scripting languages",
        "last_5_words": "and Spark streaming."
      },
      "text": "\u2022 Proficiency in scripting languages such as Python.\n\u2022 Strong command of libraries such as Pandas, Dask, and PySpark.\n\u2022 Deep understanding of SQL, NoSQL, and relational database design, and proficiency in using SQL to extract and analyze data.\n\u2022 Experience with data pipeline and workflow management tools such as Airflow, Perfect.\n\u2022 Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.\n\u2022 The ideal candidate should be familiar with working with API servers, including Flask, Django, or FastAPI.\n\u2022 Candidates should have demonstrable experience in distributed computing and managing big data systems.\n\u2022 They must be self-motivated and stay updated with the latest trends and advancements in technology.\n\u2022 Understanding of event-driven architectures and real-time processing.\n\u2022 Knowledge of streaming technologies like Apache Kafka, Apache Flink, and Spark streaming.",
      "word_count": 186
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Pandas"
    },
    {
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    },
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
      "skill_name": "NoSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Airflow"
    },
    {
      "is_primary": true,
      "skill_name": "Perfect"
    },
    {
      "is_primary": true,
      "skill_name": "Flask"
    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
      "skill_name": "Distributed Computing"
    },
    {
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    },
    {
      "is_primary": true,
      "skill_name": "Event-Driven Architecture"
    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
      "skill_name": "Apache Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Flink"
    },
    {
      "is_primary": true,
      "skill_name": "Spark Streaming"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Statistics"
    },
    {
      "is_primary": true,
      "skill_name": "Analytics"
    }
  ],
  "jd_role": {
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    "rationale": null,
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      "Big Data Engineer",
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    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
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    "role": "Big Data Analytics Developer",
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        },
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      },
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        "bullet_count": 0,
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        "source_marker": {
          "first_5_words": "The ideal candidate must be",
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        },
        "text": "The ideal candidate must be an independent problem solver with excellent communication skills. Your ability to stay up to date with the changes in the Big Data Analytics and Machine Learning technology landscape is critical for your success in the team.",
        "word_count": 40
      },
      {
        "bullet_count": 0,
        "heading": "Collaboration",
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        "source_marker": {
          "first_5_words": "This role involves collaborating with",
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        },
        "text": "This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight.",
        "word_count": 28
      },
      {
        "bullet_count": 0,
        "heading": "Must-have Skills",
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        "source_marker": {
          "first_5_words": "Candidate must know the underlying",
          "last_5_words": "must-have skill for this role."
        },
        "text": "Candidate must know the underlying mathematical foundations of statistics, machine learning, and analytics. The ability to transform a data analytics objective into a mathematical process is must must-have skill for this role.",
        "word_count": 38
      },
      {
        "bullet_count": 10,
        "heading": "Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Proficiency in scripting languages",
          "last_5_words": "and Spark streaming."
        },
        "text": "\u2022 Proficiency in scripting languages such as Python.\n\u2022 Strong command of libraries such as Pandas, Dask, and PySpark.\n\u2022 Deep understanding of SQL, NoSQL, and relational database design, and proficiency in using SQL to extract and analyze data.\n\u2022 Experience with data pipeline and workflow management tools such as Airflow, Perfect.\n\u2022 Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.\n\u2022 The ideal candidate should be familiar with working with API servers, including Flask, Django, or FastAPI.\n\u2022 Candidates should have demonstrable experience in distributed computing and managing big data systems.\n\u2022 They must be self-motivated and stay updated with the latest trends and advancements in technology.\n\u2022 Understanding of event-driven architectures and real-time processing.\n\u2022 Knowledge of streaming technologies like Apache Kafka, Apache Flink, and Spark streaming.",
        "word_count": 186
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "3b6789e7-bcb7-4872-9e38-a7b681daafa5",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight.",
            "similarity": 0.5922
          },
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "Strong command of libraries such as Pandas, Dask, and PySpark.",
            "similarity": 0.518
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics.",
            "similarity": 0.5105
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5403,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "The automated workflows must be tuned for large-scale data processing in a performant and scalable manner.",
            "similarity": 0.4726
          },
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics.",
            "similarity": 0.4328
          },
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Your ability to stay up to date with the changes in the Big Data Analytics and Machine Learning technology landscape is critical for your success in the team.",
            "similarity": 0.4021
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4358,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "You will be responsible for developing Big Data Analytics workflows driving Diagnostic, Prescriptive and Predictive analytics.",
            "similarity": 0.4423
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Strong command of libraries such as Pandas, Dask, and PySpark.",
            "similarity": 0.4112
          },
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "This role involves collaborating with domain experts to understand analytics objectives and identifying which process will be most efficient in extracting the insight.",
            "similarity": 0.4025
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4187,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "asynchronous job processing",
            "sentence": "Understanding of event-driven architectures and real-time processing.",
            "similarity": 0.4471
          },
          {
            "kra_text": "backend performance tuning",
            "sentence": "The automated workflows must be tuned for large-scale data processing in a performant and scalable manner.",
            "similarity": 0.4158
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "The ability to transform a data analytics objective into a mathematical process is must must-have skill for this role.",
            "similarity": 0.3843
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.4157,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": [
          {
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            "similarity": 0.43
          },
          {
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          },
          {
            "kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
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            "similarity": 0.3925
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        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
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    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 6,
        "matched_skills": [
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          "Apache Kafka",
          "Flink",
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        "kra_matches": null,
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    "case": "A",
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    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
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    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.29 does not contradict",
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    "centroid_updated": true,
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    "new_kra_attached": null,
    "new_skills_attached": [
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      },
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        "is_primary": true,
        "queue_id": 11999,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Dask",
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      },
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        "skill_name": "PySpark",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 12001,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Perfect",
        "status": "pending"
      },
      {
        "is_primary": true,
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        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Distributed Computing",
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      },
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        "is_primary": true,
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        "skill_name": "Big Data",
        "status": "pending"
      },
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        "is_primary": true,
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        "skill_name": "Real-Time Processing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 12005,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Statistics",
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}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "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": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "PySpark",
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      "matched_via": "embedding_alias"
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      "alias_persisted": false,
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      "existing_alias_text": "SQL",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Airflow",
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        "is_also_category": false,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1980,
      "existing_alias_text": "Flask",
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      "matched_canonical": {
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        "is_also_category": false,
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 82,
      "existing_alias_text": "Django",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1837,
      "existing_alias_text": "FastAPI",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "existing_alias_id": 2028,
      "existing_alias_text": "Distributed Systems",
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      "matched_via": "embedding_alias"
    },
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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      "matched_via": "alias"
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      "existing_alias_text": "Apache Flink",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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      "existing_alias_text": "Spark Streaming",
      "input_term": "Spark Streaming",
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2015,
      "existing_alias_text": "Machine Learning",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2634,
      "existing_alias_text": "Analytics",
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      "matched_canonical": {
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        "is_also_category": false,
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        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    },
    {
      "dimension": {
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          "role_archetype": "Engineering",
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        "slug": "d_init_01",
        "source": "db"
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        "id": 1360,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "event-driven-architecture",
        "sub_category_id": 1027,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "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": "Event-Driven Architecture",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Event-Driven Architecture",
      "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": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Real-Time Processing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "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": "real-time-processing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Kafka",
          "alias_type": "CANONICAL",
          "id": 349,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Apache Kafka",
        "id": 145,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-kafka",
        "sub_category_id": 128,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Messaging and Event Streaming",
            "id": 8,
            "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
            "slug": "messaging-and-event-streaming",
            "source": "db"
          },
          "input_skill": "Apache Kafka",
          "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": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Kafka",
      "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": "Apache Flink",
          "alias_type": "CANONICAL",
          "id": 314,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Apache Flink 1.20",
          "alias_type": "VERSION",
          "id": 318,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Apache Flink 1.x",
          "alias_type": "VERSION",
          "id": 317,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Flink 1.20",
          "alias_type": "VERSION",
          "id": 316,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Flink 1.x",
          "alias_type": "VERSION",
          "id": 315,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Apache Flink",
        "id": 120,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-flink",
        "sub_category_id": 94,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Stream Processing Systems",
            "id": 25,
            "rationale": "Technologies for processing event streams and near-real-time data flows. This includes stream transformations, windowing, stateful processing, and stream-to-warehouse delivery patterns.",
            "slug": "stream-processing-systems",
            "source": "db"
          },
          "input_skill": "Apache Flink",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Flink",
      "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": "DStreams",
          "alias_type": "VERSION",
          "id": 320,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Spark 2.x",
          "alias_type": "VERSION",
          "id": 321,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Spark 3.x",
          "alias_type": "VERSION",
          "id": 322,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Spark Streaming",
          "alias_type": "VERSION",
          "id": 319,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Spark Structured Streaming",
          "alias_type": "VERSION",
          "id": 325,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Structured Streaming",
          "alias_type": "VERSION",
          "id": 324,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Spark Streaming",
        "id": 121,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "spark-streaming",
        "sub_category_id": 94,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Stream Processing Systems",
            "id": 25,
            "rationale": "Technologies for processing event streams and near-real-time data flows. This includes stream transformations, windowing, stateful processing, and stream-to-warehouse delivery patterns.",
            "slug": "stream-processing-systems",
            "source": "db"
          },
          "input_skill": "Spark Streaming",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Spark Streaming",
      "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": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Statistics",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "statistics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Analytics",
          "alias_type": "CANONICAL",
          "id": 2634,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 37,
        "display_name": "Analytics",
        "id": 1664,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "analytics",
        "sub_category_id": 1257,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "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": "Analytics",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Analytics",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Pandas",
    "Dask",
    "Perfect",
    "Big Data",
    "Real-Time Processing",
    "Statistics"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.29 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Pandas",
      "tag": "new"
    },
    {
      "skill": "Dask",
      "tag": "new"
    },
    {
      "skill": "PySpark",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "NoSQL",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "Perfect",
      "tag": "new"
    },
    {
      "skill": "Flask",
      "tag": "in_db"
    },
    {
      "skill": "Django",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "Distributed Computing",
      "tag": "in_db"
    },
    {
      "skill": "Big Data",
      "tag": "new"
    },
    {
      "skill": "Event-Driven Architecture",
      "tag": "in_db"
    },
    {
      "skill": "Real-Time Processing",
      "tag": "new"
    },
    {
      "skill": "Apache Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Apache Flink",
      "tag": "in_db"
    },
    {
      "skill": "Spark Streaming",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Statistics",
      "tag": "new"
    },
    {
      "skill": "Analytics",
      "tag": "in_db"
    }
  ],
  "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": 2,
        "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": 2,
        "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": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": 2,
        "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 2,
        "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": 2,
        "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,
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          "source": "db"
        },
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        "roles_from_db": [
          {
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        "roles_from_db": [
          {
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        "role_dimension_saved": false,
        "roles_from_db": [
          {
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            "role_archetype": null,
            "slug": "pega-developer",
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        ],
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        "skipped_reason": null
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      {
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        "roles_from_db": [
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          "slug": "nosql-databases",
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        "roles_from_db": [
          {
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            "slug": "backend-engineer",
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        "skipped_reason": null
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      {
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        "role_dimension_saved": false,
        "roles_from_db": [
          {
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            "role_archetype": null,
            "slug": "ml-engineer",
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          {
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            "slug": "ml-ops-engineer",
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        ],
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        "skill_id": 265,
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        "skipped_reason": null
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      {
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          "slug": "d_init_01",
          "source": "db"
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        "dimension_id": 96,
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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": [],
        "skill_dimension_saved": true,
        "skill_id": 1344,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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          "rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
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          "source": "db"
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        "input_skill": "Flask",
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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": "Backend Developer",
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          {
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            "role_archetype": "Engineering",
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            "role_archetype": "Engineering",
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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",
            "slug": "python-backend-developer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
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        "skipped_reason": null
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          "rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
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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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            "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.",
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            "role_archetype": "Engineering",
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        ],
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          "slug": "d_init_01",
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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": 1201,
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        "roles_from_db": [
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            "role_archetype": "Engineering",
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        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
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            "role_archetype": "Engineering",
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          {
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        ],
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        "skill_tag": "new",
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          "source": "db"
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            "display_name": ".NET Backend Developer",
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            "role_archetype": "Engineering",
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          {
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            "role_archetype": "Engineering",
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          },
          {
            "display_name": "Python Backend Developer",
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            "role_archetype": "Engineering",
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        ],
        "skill_dimension_saved": false,
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        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
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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": "Distributed Computing",
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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": [],
        "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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          "display_name": "React Frontend Development",
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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": "Event-Driven Architecture",
        "llm_role": null,
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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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