Pipeline run
9bf75e68-d226-4099-92ae-38776ed7fbc0
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: 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.
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.
Aliases — catalog
- RDBMS (CANONICAL)
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Cloud (CANONICAL)
Context tags (catalog)
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 |
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Query Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
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 |
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
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
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Monitoring Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Aliases — catalog
- Amazon S3 (CANONICAL) primary
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Operating Systems
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Operating Systems
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- PL/SQL (CANONICAL)
Context tags (catalog)
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) |
Aliases — catalog
- domain modeling (CANONICAL) primary
- Domain Modeling (CANONICAL)
Context tags (catalog)
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
|
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)
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) |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
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
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"
],
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"roles_and_responsibilities": [
{
"bullet_count": 11,
"heading": "The Role",
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"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": [
{
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},
{
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"role_id": 143,
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},
{
"display_name": "Database Developer (SQL/PL-SQL)",
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}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
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{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
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"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.",
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"similarity": 0.5443
}
],
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},
{
"display_name": "DevOps Engineer",
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{
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"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.",
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}
],
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},
{
"display_name": "Frontend Developer",
"kra_matches": [
{
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},
{
"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.",
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},
{
"kra_text": "Optimizes web application performance through code splitting, lazy loading, tree shaking, bundle size reduction, and Core Web Vitals improvements.",
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],
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},
{
"display_name": "Fullstack Developer",
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{
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},
{
"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.",
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{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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}
],
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},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
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{
"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.",
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}
],
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}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
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"matched_count": 6,
"matched_skills": [
"Amazon S3",
"Apache Spark",
"Cloud",
"Hadoop",
"Python",
"SQL"
],
"role_id": 2,
"score": 0.3333,
"slug": "data-engineer",
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},
{
"display_name": "Fullstack Developer",
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"matched_skills": [
"Cloud",
"Python"
],
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"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
{
"alias_matches": [
{
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"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \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"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Oracle",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "oracle",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "AWS",
"alias_type": "CANONICAL",
"id": 406,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "AWS",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"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,
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