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Pipeline run

9bf75e68-d226-4099-92ae-38776ed7fbc0

Pipeline LLM cost (USD)
API 1: $0.0045 API 2: $0.0004 API 3: $0.0000 Total: $0.0048

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 · Pipeline operations and incident response
Maintain and tune Oracle/Hadoop/cloud data platforms, write PL/SQL, Hive/Impala and PySpark jobs, and automate/schedule them with Autosys while troubleshooting failed loads, job issues, and performance bottlenecks.
""Monitoring and troubleshooting the scheduled jobs using Autosys.""
Tech stack maturity
Mainstream Modern
The skill set centers on established modern data engineering technologies like cloud, S3, Spark, Python, SQL, and RDBMS, which are widely used in current production environments without being bleeding-edge.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 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):
Evidence — skills matched in JD (20)
RDBMS Big Data Cloud Hadoop HiveSQL Impala Apache Spark PySpark Autosys Amazon S3 Amazon Athena Unix Linux PL/SQL Data Modeling Python SQL Oracle Cloudera CDP AWS
Skill cluster (5 dimension groups, role-scoped)
Cloud Platforms
Cloud AWS
ETL and ELT Tooling
Hadoop Apache Spark
Programming Languages for Data Work
Python SQL
Cloud Storage and File Formats
Amazon S3
Cross-cutting / unaligned
RDBMS Big Data HiveSQL Impala PySpark Autosys Amazon Athena Unix Linux PL/SQL Data Modeling Oracle Cloudera CDP
Show KRA description ↓
• Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud. • Applies knowledge of general business functions and systems to develop IT strategies, operational plans, and standards to support achievement of department, business, and corporate goals. • Responsible for partnership with internal and external stakeholders to ensure high quality delivery of information technology solutions. • Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests. • Recommends system solutions by comparing advantages and disadvantages of custom development and purchase alternatives. • Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms. • Identify opportunities to fine-tune and optimize applications. • Hands on experience needed in Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) for enhancement of any projects. • Monitoring and troubleshooting the scheduled jobs using Autosys. • Monitoring and troubleshooting issues with the data loads to S3, Athena databases in the Cloud. • Ensures daily deliverables complete in time for clients and monitors schedules that involves adjusting the priority of the jobs, usage of space, memory, and resources. • Automates any repeated manual process using Autosys scheduler and Unix/ Linux. • Know the key concepts like procedures, functions, triggers, constraints, package, Sub Programs, PL/SQL error handling etc. • Performance Tuning, Data Modeling and Unix /Python Scripting. • Aware about code optimization techniques. • Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) (Nice to have) • SQL / RDBMS – Relational Database Management Systems • Relational Database Concepts • Client / Server Concepts • Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting – Aware about Oracle installation, database creation, maintenance and performance tuning. • Behavioral traits such as right attitude, motivation, taking initiatives, organizational skills and effective time management. • Excellent written and oral communication skills. • Being a team player as well as ability to work independently with reliability and integrity. • Ability to establish and maintain productive working relationships in multi-disciplinary teams with good adaptability, flexibility, and negotiation skills. • 2+ years of experience in Hadoop Ecosystem Hive, Impala, Pyspark. (Cloudera – CDP Preferred). • 2+ years of experience in scheduling Autosys jobs. • Knowledge in cloud platform/ tools (S3, Athena) concepts and trends. AWS preferred. • Expert troubleshooting skills.

Signals

Skill data-engineer
0.33
Alias data-engineer
1.00
KRA data-engineer
0.56
Status: completed Created: 2026-05-27T16:53:19.453900Z Updated: 2026-05-27T16:55:29.960032Z API 3 duration: 51297 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

Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer

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

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

Job description

At ICON, it’s our people that set us apart. Our diverse teams enable us to become a better partner to our customers and help us to fulfil our mission to advance and improve patients’ lives.

Our ‘Own It’ culture is driven by four key values that bring us together as individuals and set us apart as an organisation: Accountability & Delivery, Collaboration, Partnership and Integrity. We want to be the Clinical Research Organisation that delivers excellence to our clients and to patients at every touch-point. In short, to be the partner of choice in drug development.

That’s our vision. We’re driven by it. And we need talented people who share it.

If you’re as driven as we are, join us. You’ll be working in a dynamic and supportive environment, with some of the brightest and the friendliest people in the sector, and you’ll be helping shape an industry.

Job Description Summary

Symphony Health, part of the ICON plc family, is a leading provider of high-value data, analytics, technology solutions and actionable insights for healthcare and life sciences manufacturers, payers and providers.

The company helps clients drive revenue growth and commercial effectiveness, while adapting to the transformation of the healthcare ecosystem, by integrating a broad set of patient, prescriber, payer and clinical data together with primary and secondary health research, analytics and consulting.

Symphony Health delivers a comprehensive perspective on the real dynamics that drive business in the healthcare and life sciences markets.

The Role

• Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud. 
• Applies knowledge of general business functions and systems to develop IT strategies, operational plans, and standards to support achievement of department, business, and corporate goals. 
• Responsible for partnership with internal and external stakeholders to ensure high quality delivery of information technology solutions. 
• Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests. 
• Recommends system solutions by comparing advantages and disadvantages of custom development and purchase alternatives. 
• Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms. 
• Identify opportunities to fine-tune and optimize applications. 
• Hands on experience needed in Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) for enhancement of any projects. 
• Monitoring and troubleshooting the scheduled jobs using Autosys. 
• Monitoring and troubleshooting issues with the data loads to S3, Athena databases in the Cloud. 
• Ensures daily deliverables complete in time for clients and monitors schedules that involves adjusting the priority of the jobs, usage of space, memory, and resources. 
• Automates any repeated manual process using Autosys scheduler and Unix/ Linux.


Preferred Knowledge/ Experience:

• Know the key concepts like procedures, functions, triggers, constraints, package, Sub Programs, PL/SQL error handling etc. 
• Performance Tuning, Data Modeling and Unix /Python Scripting. 
• Aware about code optimization techniques. 
• Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) (Nice to have) 
• SQL / RDBMS – Relational Database Management Systems 
• Relational Database Concepts 
• Client / Server Concepts


Desired Competencies (Technical/Behavioral Competency)

• Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting – Aware about Oracle installation, database creation, maintenance and performance tuning. 
• Behavioral traits such as right attitude, motivation, taking initiatives, organizational skills and effective time management. 
• Excellent written and oral communication skills. 
• Being a team player as well as ability to work independently with reliability and integrity. 
• Ability to establish and maintain productive working relationships in multi-disciplinary teams with good adaptability, flexibility, and negotiation skills. 
• 2+ years of experience in Hadoop Ecosystem Hive, Impala, Pyspark. (Cloudera – CDP Preferred). 
• 2+ years of experience in scheduling Autosys jobs. 
• Knowledge in cloud platform/ tools (S3, Athena) concepts and trends. AWS preferred. 
• Expert troubleshooting skills.


Benefits of Working in ICON:

Our success depends on the quality of our people. That’s why we’ve made it a priority to build a culture that rewards high performance and nurtures talent.

We offer very competitive salary packages. And to keep them competitive, we regularly benchmark them against our competitors. Our annual bonuses reflect delivery of performance goals – both ours and yours.

We also provide a range of health-related benefits to employees and their families and offer competitive retirement plans – and related benefits such as life assurance – so you can save and plan with confidence for the years ahead.

But beyond the competitive salaries and comprehensive benefits, you’ll benefit from an environment where you are encouraged to fulfil your sense of purpose and drive lasting change.

ICON is an equal opportunity and inclusive employer and is committed to providing a workplace free of discrimination and harassment. All qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please let us know through the form below.

Skills from this JD

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

RDBMS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RDBMS id=1355 · rdbms

Aliases — catalog

  • RDBMS (CANONICAL)

Context tags (catalog)

ACID ER diagrams SQL backup and recovery data integrity data modeling database tuning foreign keys indexes joins normalization query optimization schema design stored procedures transactions views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database Management System
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: RDBMS is a core requirement in many job descriptions across backend, data, and DBA roles; PostgreSQL, MySQL, and SQL Server remain standard enterprise stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
1023
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)
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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Cloud Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cloud id=1572 · cloud

Aliases — catalog

  • Cloud (CANONICAL)

Context tags (catalog)

AWS Azure DevOps Docker Google Cloud IaaS Kubernetes PaaS SaaS cloud migration cloud security hybrid cloud microservices multi-cloud serverless

Stored enrichment (catalog DB)

Category
Domain
Sub-category
Cloud Computing
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Cloud is a hiring-pipeline staple: AWS, Azure, and GCP appear in a large share of modern infrastructure JDs, and major vendors continue expanding cloud services rather than sunsetting them.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
37
Sub-category id
1177
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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Hadoop Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hadoop id=1351 · hadoop

Aliases — catalog

  • Hadoop (CANONICAL)

Context tags (catalog)

Big Data Data Lake Distributed Computing ELT ETL Flume HDFS Hive Kafka MapReduce NoSQL Oozie Pig Spark Sqoop YARN

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
91
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Cloudera CDP Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
HiveSQL 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
Query Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Impala Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
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
Autosys 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
Monitoring Tools
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Amazon S3 Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon S3 id=170 · amazon-s3

Aliases — catalog

  • Amazon S3 (CANONICAL) primary

Context tags (catalog)

ACL Cross-Region Replication Glacier SSE-KMS SSE-S3 access control bucket bucket policy cross-region replication event notifications lifecycle policy multipart upload object storage pre-signed URL replication static website hosting storage class versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2006
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Amazon Athena Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Unix 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
Operating Systems
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Linux 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
Operating Systems
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
PL/SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PL/SQL id=1567 · pl-sql

Aliases — catalog

  • PL/SQL (CANONICAL)

Context tags (catalog)

Oracle PL/SQL blocks SQL*Plus bulk collect cursors data manipulation data types dynamic SQL exception handling functions packages performance tuning stored procedures transaction control triggers

Stored enrichment (catalog DB)

Category
Language
Sub-category
Procedural Sql Language
Vendor
Oracle Corporation
License
proprietary
Year introduced
1990
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PL/SQL appears frequently in Oracle-focused job postings and remains a standard skill for Oracle database development and maintenance; it is not sunset or replaced by a newer successor.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
1173
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)
Data Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain modeling id=2379 · domain-modeling

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

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

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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 & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • 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 & DSLs
programming-languages-dsls
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)
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 & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • 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 & DSLs
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
Oracle Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
AWS Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

API Gateway AWS CLI Auto Scaling CloudFormation CloudFront CloudTrail CloudWatch Cognito DynamoDB EC2 ECS EKS Elastic Beanstalk Elastic Load Balancing IAM KMS Lambda RDS Route 53 S3 SNS SQS Serverless VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Amazon
License
other_open
Year introduced
2006
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
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
RDBMS in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
PySpark new
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Amazon S3 in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
PL/SQL in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
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 & DSLs
programming-languages-dsls
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)
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 & DSLs
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
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Big Data | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Cloudera CDP | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed HiveSQL | type=Query Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR
canonical_skill_proposed Impala | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Autosys | type=Monitoring Tools subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Amazon Athena | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Unix | type=Operating Systems subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Linux | type=Operating Systems subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed PySpark ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Platform Engineer
CompanyICON
ExperienceAtleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting
DomainHealthcare
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "At ICON, it\u2019s our people",
      "last_5_words": "partner of choice in drug development."
    },
    "text": "At ICON, it\u2019s our people that set us apart. Our diverse teams enable us to become a better partner to our customers and help us to fulfil our mission to advance and improve patients\u2019 lives.\n\nOur \u2018Own It\u2019 culture is driven by four key values that bring us together as individuals and set us apart as an organisation: Accountability \u0026 Delivery, Collaboration, Partnership and Integrity. We want to be the Clinical Research Organisation that delivers excellence to our clients and to patients at every touch-point. In short, to be the partner of choice in drug development.",
    "word_count": 84
  },
  "certifications": [],
  "company_name": "ICON",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "HealthTech",
        "Life Sciences"
      ],
      "domain": "Healthcare"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 7,
    "raw": "Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting"
  },
  "job_locations": [],
  "role": "Data Platform Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Analyst",
    "Database Developer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 11,
      "heading": "The Role",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Maintain highly scalable data",
        "last_5_words": "using Autosys scheduler and Unix/ Linux."
      },
      "text": "\u2022 Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud.\n\u2022 Applies knowledge of general business functions and systems to develop IT strategies, operational plans, and standards to support achievement of department, business, and corporate goals.\n\u2022 Responsible for partnership with internal and external stakeholders to ensure high quality delivery of information technology solutions.\n\u2022 Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests.\n\u2022 Recommends system solutions by comparing advantages and disadvantages of custom development and purchase alternatives.\n\u2022 Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms.\n\u2022 Identify opportunities to fine-tune and optimize applications.\n\u2022 Hands on experience needed in Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) for enhancement of any projects.\n\u2022 Monitoring and troubleshooting the scheduled jobs using Autosys.\n\u2022 Monitoring and troubleshooting issues with the data loads to S3, Athena databases in the Cloud.\n\u2022 Ensures daily deliverables complete in time for clients and monitors schedules that involves adjusting the priority of the jobs, usage of space, memory, and resources.\n\u2022 Automates any repeated manual process using Autosys scheduler and Unix/ Linux.",
      "word_count": 263
    },
    {
      "bullet_count": 7,
      "heading": "Preferred Knowledge/ Experience",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Know the key concepts like",
        "last_5_words": "Client / Server Concepts"
      },
      "text": "\u2022 Know the key concepts like procedures, functions, triggers, constraints, package, Sub Programs, PL/SQL error handling etc.\n\u2022 Performance Tuning, Data Modeling and Unix /Python Scripting.\n\u2022 Aware about code optimization techniques.\n\u2022 Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) (Nice to have)\n\u2022 SQL / RDBMS \u2013 Relational Database Management Systems\n\u2022 Relational Database Concepts\n\u2022 Client / Server Concepts",
      "word_count": 66
    },
    {
      "bullet_count": 9,
      "heading": "Desired Competencies (Technical/Behavioral Competency)",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Atleast 7 years hands on",
        "last_5_words": "and expert troubleshooting skills."
      },
      "text": "\u2022 Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting \u2013 Aware about Oracle installation, database creation, maintenance and performance tuning.\n\u2022 Behavioral traits such as right attitude, motivation, taking initiatives, organizational skills and effective time management.\n\u2022 Excellent written and oral communication skills.\n\u2022 Being a team player as well as ability to work independently with reliability and integrity.\n\u2022 Ability to establish and maintain productive working relationships in multi-disciplinary teams with good adaptability, flexibility, and negotiation skills.\n\u2022 2+ years of experience in Hadoop Ecosystem Hive, Impala, Pyspark. (Cloudera \u2013 CDP Preferred).\n\u2022 2+ years of experience in scheduling Autosys jobs.\n\u2022 Knowledge in cloud platform/ tools (S3, Athena) concepts and trends. AWS preferred.\n\u2022 Expert troubleshooting skills.",
      "word_count": 139
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "RDBMS"
    },
    {
      "is_primary": true,
      "skill_name": "Big Data"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud"
    },
    {
      "is_primary": true,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": false,
      "skill_name": "Cloudera CDP"
    },
    {
      "is_primary": true,
      "skill_name": "HiveSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Impala"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Spark"
    },
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
      "skill_name": "Autosys"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon S3"
    },
    {
      "is_primary": true,
      "skill_name": "Amazon Athena"
    },
    {
      "is_primary": true,
      "skill_name": "Unix"
    },
    {
      "is_primary": true,
      "skill_name": "Linux"
    },
    {
      "is_primary": true,
      "skill_name": "PL/SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": false,
      "skill_name": "AWS"
    }
  ],
  "jd_role": {
    "display_name": "Data Platform Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Analyst",
      "Database Developer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "At ICON, it\u2019s our people",
        "last_5_words": "partner of choice in drug development."
      },
      "text": "At ICON, it\u2019s our people that set us apart. Our diverse teams enable us to become a better partner to our customers and help us to fulfil our mission to advance and improve patients\u2019 lives.\n\nOur \u2018Own It\u2019 culture is driven by four key values that bring us together as individuals and set us apart as an organisation: Accountability \u0026 Delivery, Collaboration, Partnership and Integrity. We want to be the Clinical Research Organisation that delivers excellence to our clients and to patients at every touch-point. In short, to be the partner of choice in drug development.",
      "word_count": 84
    },
    "certifications": [],
    "company_name": "ICON",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "HealthTech",
          "Life Sciences"
        ],
        "domain": "Healthcare"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 7,
      "raw": "Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting"
    },
    "job_locations": [],
    "role": "Data Platform Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Analyst",
      "Database Developer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 11,
        "heading": "The Role",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Maintain highly scalable data",
          "last_5_words": "using Autosys scheduler and Unix/ Linux."
        },
        "text": "\u2022 Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud.\n\u2022 Applies knowledge of general business functions and systems to develop IT strategies, operational plans, and standards to support achievement of department, business, and corporate goals.\n\u2022 Responsible for partnership with internal and external stakeholders to ensure high quality delivery of information technology solutions.\n\u2022 Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests.\n\u2022 Recommends system solutions by comparing advantages and disadvantages of custom development and purchase alternatives.\n\u2022 Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms.\n\u2022 Identify opportunities to fine-tune and optimize applications.\n\u2022 Hands on experience needed in Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) for enhancement of any projects.\n\u2022 Monitoring and troubleshooting the scheduled jobs using Autosys.\n\u2022 Monitoring and troubleshooting issues with the data loads to S3, Athena databases in the Cloud.\n\u2022 Ensures daily deliverables complete in time for clients and monitors schedules that involves adjusting the priority of the jobs, usage of space, memory, and resources.\n\u2022 Automates any repeated manual process using Autosys scheduler and Unix/ Linux.",
        "word_count": 263
      },
      {
        "bullet_count": 7,
        "heading": "Preferred Knowledge/ Experience",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Know the key concepts like",
          "last_5_words": "Client / Server Concepts"
        },
        "text": "\u2022 Know the key concepts like procedures, functions, triggers, constraints, package, Sub Programs, PL/SQL error handling etc.\n\u2022 Performance Tuning, Data Modeling and Unix /Python Scripting.\n\u2022 Aware about code optimization techniques.\n\u2022 Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) (Nice to have)\n\u2022 SQL / RDBMS \u2013 Relational Database Management Systems\n\u2022 Relational Database Concepts\n\u2022 Client / Server Concepts",
        "word_count": 66
      },
      {
        "bullet_count": 9,
        "heading": "Desired Competencies (Technical/Behavioral Competency)",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Atleast 7 years hands on",
          "last_5_words": "and expert troubleshooting skills."
        },
        "text": "\u2022 Atleast 7 years hands on experience in Oracle PL/SQL UNIX / LINUX/ Python Scripting \u2013 Aware about Oracle installation, database creation, maintenance and performance tuning.\n\u2022 Behavioral traits such as right attitude, motivation, taking initiatives, organizational skills and effective time management.\n\u2022 Excellent written and oral communication skills.\n\u2022 Being a team player as well as ability to work independently with reliability and integrity.\n\u2022 Ability to establish and maintain productive working relationships in multi-disciplinary teams with good adaptability, flexibility, and negotiation skills.\n\u2022 2+ years of experience in Hadoop Ecosystem Hive, Impala, Pyspark. (Cloudera \u2013 CDP Preferred).\n\u2022 2+ years of experience in scheduling Autosys jobs.\n\u2022 Knowledge in cloud platform/ tools (S3, Athena) concepts and trends. AWS preferred.\n\u2022 Expert troubleshooting skills.",
        "word_count": 139
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "9bf75e68-d226-4099-92ae-38776ed7fbc0",
  "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
      },
      {
        "display_name": "Data Analyst",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 143,
        "score": 1.0,
        "slug": "data-analyst",
        "total_count": null
      },
      {
        "display_name": "Database Developer (SQL/PL-SQL)",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 201,
        "score": 1.0,
        "slug": "database-developer-sql-pl-sql",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
            "sentence": "Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests.",
            "similarity": 0.5644
          },
          {
            "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": "Hadoop Ecosystem Cloudera CDP (Preferred), HiveSQL, Impala, Apache Spark (PySpark) (Nice to have)",
            "similarity": 0.5618
          },
          {
            "kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
            "sentence": "Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud.",
            "similarity": 0.5443
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5568,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms.",
            "similarity": 0.5769
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Ensures daily deliverables complete in time for clients and monitors schedules that involves adjusting the priority of the jobs, usage of space, memory, and resources.",
            "similarity": 0.5528
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Identify opportunities to fine-tune and optimize applications.",
            "similarity": 0.5152
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.5483,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "Optimizes web application performance through code splitting, lazy loading, tree shaking, bundle size reduction, and Core Web Vitals improvements.",
            "sentence": "Aware about code optimization techniques.",
            "similarity": 0.5709
          },
          {
            "kra_text": "Supports production releases by diagnosing cross-browser compatibility issues, investigating UI regressions, and resolving frontend performance degradation.",
            "sentence": "Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms.",
            "similarity": 0.5494
          },
          {
            "kra_text": "Optimizes web application performance through code splitting, lazy loading, tree shaking, bundle size reduction, and Core Web Vitals improvements.",
            "sentence": "Identify opportunities to fine-tune and optimize applications.",
            "similarity": 0.5124
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 7,
        "score": 0.5442,
        "slug": "frontend-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Designs and develops database by setting expectations and features priorities throughout development life cycle; determining design methodologies and tool sets; completing programming using languages and software products; designing and conducting tests.",
            "similarity": 0.5776
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Completes applications development by coordinating requirements, schedules, and activities; contributing to team meetings; troubleshooting development and production problems across multiple environments and operating platforms.",
            "similarity": 0.551
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud.",
            "similarity": 0.5013
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.5433,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
          {
            "kra_text": "performance tuning",
            "sentence": "Identify opportunities to fine-tune and optimize applications.",
            "similarity": 0.5738
          },
          {
            "kra_text": "performance tuning",
            "sentence": "Aware about code optimization techniques.",
            "similarity": 0.5315
          },
          {
            "kra_text": "backend data integration",
            "sentence": "Maintain highly scalable data platform and analytical solutions including but not limited to the following technologies RDBMS / Big Data / Cloud.",
            "similarity": 0.4795
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 92,
        "score": 0.5282,
        "slug": "svelte-frontend-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 6,
        "matched_skills": [
          "Amazon S3",
          "Apache Spark",
          "Cloud",
          "Hadoop",
          "Python",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.3333,
        "slug": "data-engineer",
        "total_count": 18
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Cloud",
          "Python"
        ],
        "role_id": 15,
        "score": 0.1111,
        "slug": "full-stack-engineer",
        "total_count": 18
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Cloud",
          "Python"
        ],
        "role_id": 3,
        "score": 0.1111,
        "slug": "ml-engineer",
        "total_count": 18
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Cloud",
          "Python"
        ],
        "role_id": 1,
        "score": 0.1111,
        "slug": "backend-engineer",
        "total_count": 18
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Cloud",
          "Python"
        ],
        "role_id": 5,
        "score": 0.1111,
        "slug": "cybersecurity-engineer",
        "total_count": 18
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": true,
    "case": "A",
    "chosen_role": {
      "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
    },
    "confidence": 0.95,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer",
    "sub_role": null
  },
  "stage5_updates": null
}
API 2 — extract-details
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2014,
      "existing_alias_text": "RDBMS",
      "input_term": "RDBMS",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "RDBMS",
        "id": 1355,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "rdbms",
        "sub_category_id": 1023,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2518,
      "existing_alias_text": "Cloud",
      "input_term": "Cloud",
      "matched_canonical": {
        "category_id": 37,
        "display_name": "Cloud",
        "id": 1572,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "cloud",
        "sub_category_id": 1177,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Hadoop",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Hadoop",
        "id": 1351,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "hadoop",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "Apache Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "PySpark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 379,
      "existing_alias_text": "Amazon S3",
      "input_term": "Amazon S3",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Amazon S3",
        "id": 170,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-s3",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2513,
      "existing_alias_text": "PL/SQL",
      "input_term": "PL/SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "PL/SQL",
        "id": 1567,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "pl-sql",
        "sub_category_id": 1173,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
      "input_term": "Data Modeling",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 406,
      "existing_alias_text": "AWS",
      "input_term": "AWS",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "AWS",
        "id": 187,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
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      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
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      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
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      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
    },
    {
      "display_name": "Go Backend Developer",
      "id": 81,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
      "id": 79,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
      "id": 16,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-ops-engineer",
      "source": "db"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Cloud Architect",
      "id": 9,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 435,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "fullstack-developer",
      "source": "db"
    },
    {
      "display_name": "Engineering Manager",
      "id": 121,
      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
      "source": "db"
    },
    {
      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "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": "RDBMS",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platforms",
        "id": 20,
        "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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        "source": "db"
      },
      "input_skill": "Cloud",
      "llm_role": null,
      "roles_from_db": [
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
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        {
          "display_name": "Cyber Security Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "devops-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
          "id": 15,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "Go Backend Developer",
          "id": 81,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "go-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Java Backend Developer",
          "id": 79,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "java-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
          "id": 84,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        },
        {
          "display_name": "MLOps Engineer",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-ops-engineer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Hadoop",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
        "id": 24,
        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
        "slug": "etl-and-elt-tooling",
        "source": "db"
      },
      "input_skill": "Apache Spark",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "ETL and ELT Tooling",
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      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Big Data",
    "Cloudera CDP",
    "HiveSQL",
    "Impala",
    "Autosys",
    "Amazon Athena",
    "Unix",
    "Linux",
    "Oracle"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Engineer",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "RDBMS",
      "tag": "in_db"
    },
    {
      "skill": "Big Data",
      "tag": "new"
    },
    {
      "skill": "Cloud",
      "tag": "in_db"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "Cloudera CDP",
      "tag": "new"
    },
    {
      "skill": "HiveSQL",
      "tag": "new"
    },
    {
      "skill": "Impala",
      "tag": "new"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "PySpark",
      "tag": "in_db"
    },
    {
      "skill": "Autosys",
      "tag": "new"
    },
    {
      "skill": "Amazon S3",
      "tag": "in_db"
    },
    {
      "skill": "Amazon Athena",
      "tag": "new"
    },
    {
      "skill": "Unix",
      "tag": "new"
    },
    {
      "skill": "Linux",
      "tag": "new"
    },
    {
      "skill": "PL/SQL",
      "tag": "in_db"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "AWS",
      "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": "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"
        },
        "dimension_id": 96,
        "input_skill": "RDBMS",
        "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": [],
        "skill_dimension_saved": true,
        "skill_id": 1355,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms",
          "id": 20,
          "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
          "slug": "cloud-platforms",
          "source": "db"
        },
        "dimension_id": 20,
        "input_skill": "Cloud",
        "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": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1572,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Hadoop",
        "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": 1351,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Apache Spark",
        "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": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "PySpark",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Amazon S3",
        "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 Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Amazon S3",
        "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": 170,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "PL/SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1567,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
          "source": "db"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "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": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "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 \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "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": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "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": {
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