Pipeline run
637a0434-a646-4412-b9e6-057b5901d2e4
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
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
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.30 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Role – Sr. Data Engineer Work Location - Mumbai Leading Private Bank Job Description: Roles & Responsibilities: · Proven experience in in SQL, Python, Hadoop ecosystem · Have worked on multiple TBs of data volume from ingestion to consumption · Work with business stakeholders to identify and document high impact business problems and potential solutions · Good understanding of Data Lake/Lakehouse architecture and experience/exposure to Hadoop (cloudera, hortonworks) and/or AWS · Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer. Versed with API and its usability · A suitable candidate will also be proficient in Spark, Spark Streaming, hive, SQLs · A suitable candidate will also demonstrate experience with big data infrastructure inclusive of MapReduce, Hive, HDFS, HBase, Oozie, etc. · The candidate will additionally demonstrate substantial experience and a deep knowledge of relational databases. · Secondary Responsibilities : · Excellent oral and written communication skills Learn and use internally available analytic technologies · Identify key performance indicators and create educational/deliverables path to achieve the same · Use educational background in data engineering and perform data mining analysis · Work with BI analysts/engineers to create prototypes · Engage in the delivery and presentation of solutions Requirements : Minimum 4 Years of Experience Knowledge of SQL, AWS, Python, Hadoop, Big Data, Spark
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
- 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 for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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 and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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 |
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
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) |
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
- DStreams (VERSION)
- Spark 2.x (VERSION)
- Spark 3.x (VERSION)
- Spark Streaming (VERSION)
- Spark Structured Streaming (VERSION)
- Structured Streaming (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Stream Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- Structured Streaming (Spark 2.0+)
Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 94
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Stream Processing Systems Catalog dimension db id 25
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Stream Processing Systems
stream-processing-systems
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Hive (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Local Key Value Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2242
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Local Persistence and Offline Behavior Catalog dimension db id 85
Library dimension (catalog)
Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- HBase (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Wide Column Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: HBase appears in a limited set of big-data/legacy Hadoop job postings, while newer JDs more often specify DynamoDB, Bigtable, or Cassandra; its market demand is specialized rather than broad.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 31
- 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
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- API (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Application Programming Interface
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: APIs are a core requirement in most software engineering JDs and underpin common integrations across cloud, mobile, and web stacks; major vendors like AWS, Stripe, and Google Cloud center products on API-first usage.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1174
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
API Integration and Data Fetching Catalog dimension db id 127
Library dimension (catalog)
Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- 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
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
React Frontend Development
d_init_01
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Lakehouse (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Platform Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Lakehouse is increasingly listed in data-platform JDs and vendor docs (Databricks, Snowflake, Microsoft Fabric), but it is not yet as universal as core warehouse or lake skills.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1026
- 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
- Data Engineering Tools
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Data Transform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Transformation Concept
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data transformation is a core ETL/ELT concept and appears broadly across job descriptions for analytics, data engineering, and BI roles; it’s a standard pipeline requirement rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1445
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Pages and Data Modeling Catalog dimension db id 254
Library dimension (catalog)
Roles linked in library: Pega Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Pages and Data Modeling
data-pages-and-data-modeling
|
— | — |
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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Relational Databases (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Relational Database Management
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Relational databases remain a hiring staple across most backend/data JDs, with PostgreSQL, MySQL, and SQL Server appearing routinely; cloud vendors also center managed RDBMS offerings, signaling broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1018
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | 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) | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark Streaming | in_db |
Stream Processing Systems
stream-processing-systems
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hive | in_db |
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| HBase | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| API | in_db |
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Lake | new |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Lake | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Lakehouse | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Transformation | new |
Data Pages and Data Modeling
data-pages-and-data-modeling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Relational Databases | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | MapReduce | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Oozie | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Big Data | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Data Ingestion | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Consumption | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Mining | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Business Intelligence | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Lake ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Data Lake ↔ React Frontend Development | |
| dimension_skill_link_proposed | Data Transformation ↔ Data Pages and Data Modeling |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Leading Private Bank",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 4,
"raw": "Minimum 4 Years of Experience"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Sr. Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Roles \u0026 Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u00b7 Proven experience in in",
"last_5_words": "and a deep knowledge of relational databases."
},
"text": "\u00b7 Proven experience in in SQL, Python, Hadoop ecosystem\n\u00b7 Have worked on multiple TBs of data volume from ingestion to consumption\n\u00b7 Work with business stakeholders to identify and document high impact business problems and potential solutions\n\u00b7 Good understanding of Data Lake/Lakehouse architecture and experience/exposure to Hadoop (cloudera, hortonworks) and/or AWS\n\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer. Versed with API and its usability\n\u00b7 A suitable candidate will also be proficient in Spark, Spark Streaming, hive, SQLs\n\u00b7 A suitable candidate will also demonstrate experience with big data infrastructure inclusive of MapReduce, Hive, HDFS, HBase, Oozie, etc.\n\u00b7 The candidate will additionally demonstrate substantial experience and a deep knowledge of relational databases.",
"word_count": 139
},
{
"bullet_count": 5,
"heading": "Secondary Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u00b7 Excellent oral and written",
"last_5_words": "delivery and presentation of solutions"
},
"text": "\u00b7 Excellent oral and written communication skills Learn and use internally available analytic technologies\n\u00b7 Identify key performance indicators and create educational/deliverables path to achieve the same\n\u00b7 Use educational background in data engineering and perform data mining analysis\n\u00b7 Work with BI analysts/engineers to create prototypes\n\u00b7 Engage in the delivery and presentation of solutions",
"word_count": 83
},
{
"bullet_count": 0,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Minimum 4 Years of Experience",
"last_5_words": "Hadoop, Big Data, Spark"
},
"text": "Minimum 4 Years of Experience\nKnowledge of SQL, AWS, Python, Hadoop, Big Data, Spark",
"word_count": 14
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "Hadoop"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": true,
"skill_name": "Spark Streaming"
},
{
"is_primary": true,
"skill_name": "Hive"
},
{
"is_primary": true,
"skill_name": "MapReduce"
},
{
"is_primary": true,
"skill_name": "HDFS"
},
{
"is_primary": true,
"skill_name": "HBase"
},
{
"is_primary": true,
"skill_name": "Oozie"
},
{
"is_primary": true,
"skill_name": "API"
},
{
"is_primary": true,
"skill_name": "Data Lake"
},
{
"is_primary": true,
"skill_name": "Lakehouse"
},
{
"is_primary": true,
"skill_name": "Big Data"
},
{
"is_primary": true,
"skill_name": "Data Ingestion"
},
{
"is_primary": true,
"skill_name": "Data Transformation"
},
{
"is_primary": true,
"skill_name": "Data Consumption"
},
{
"is_primary": true,
"skill_name": "Data Mining"
},
{
"is_primary": false,
"skill_name": "Business Intelligence"
},
{
"is_primary": true,
"skill_name": "Relational Databases"
}
],
"jd_role": {
"display_name": "Sr. Data Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Leading Private Bank",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 4,
"raw": "Minimum 4 Years of Experience"
},
"job_locations": [
{
"aliases": [
"Bombay"
],
"city": "Mumbai",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Sr. Data Engineer",
"role_aliases": [
"Data Engineer",
"Senior Data Engineer",
"Big Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Roles \u0026 Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u00b7 Proven experience in in",
"last_5_words": "and a deep knowledge of relational databases."
},
"text": "\u00b7 Proven experience in in SQL, Python, Hadoop ecosystem\n\u00b7 Have worked on multiple TBs of data volume from ingestion to consumption\n\u00b7 Work with business stakeholders to identify and document high impact business problems and potential solutions\n\u00b7 Good understanding of Data Lake/Lakehouse architecture and experience/exposure to Hadoop (cloudera, hortonworks) and/or AWS\n\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer. Versed with API and its usability\n\u00b7 A suitable candidate will also be proficient in Spark, Spark Streaming, hive, SQLs\n\u00b7 A suitable candidate will also demonstrate experience with big data infrastructure inclusive of MapReduce, Hive, HDFS, HBase, Oozie, etc.\n\u00b7 The candidate will additionally demonstrate substantial experience and a deep knowledge of relational databases.",
"word_count": 139
},
{
"bullet_count": 5,
"heading": "Secondary Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u00b7 Excellent oral and written",
"last_5_words": "delivery and presentation of solutions"
},
"text": "\u00b7 Excellent oral and written communication skills Learn and use internally available analytic technologies\n\u00b7 Identify key performance indicators and create educational/deliverables path to achieve the same\n\u00b7 Use educational background in data engineering and perform data mining analysis\n\u00b7 Work with BI analysts/engineers to create prototypes\n\u00b7 Engage in the delivery and presentation of solutions",
"word_count": 83
},
{
"bullet_count": 0,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Minimum 4 Years of Experience",
"last_5_words": "Hadoop, Big Data, Spark"
},
"text": "Minimum 4 Years of Experience\nKnowledge of SQL, AWS, Python, Hadoop, Big Data, Spark",
"word_count": 14
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "637a0434-a646-4412-b9e6-057b5901d2e4",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer.",
"similarity": 0.5901
},
{
"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": "\u00b7 A suitable candidate will also be proficient in Spark, Spark Streaming, hive, SQLs",
"similarity": 0.5627
},
{
"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": "\u00b7 Work with BI analysts/engineers to create prototypes",
"similarity": 0.5369
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5632,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "\u00b7 Work with BI analysts/engineers to create prototypes",
"similarity": 0.5103
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "\u00b7 Proven experience in in SQL, Python, Hadoop ecosystem",
"similarity": 0.4192
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "\u00b7 A suitable candidate will also be proficient in Spark, Spark Streaming, hive, SQLs",
"similarity": 0.4002
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4432,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer.",
"similarity": 0.4661
},
{
"kra_text": "backend data integration",
"sentence": "\u00b7 Have worked on multiple TBs of data volume from ingestion to consumption",
"similarity": 0.4246
},
{
"kra_text": "backend data integration",
"sentence": "\u00b7 Work with BI analysts/engineers to create prototypes",
"similarity": 0.3719
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.4209,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "\u00b7 Work with BI analysts/engineers to create prototypes",
"similarity": 0.4826
},
{
"kra_text": "integrate external APIs and data sources",
"sentence": "\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer.",
"similarity": 0.3822
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "\u00b7 Engage in the delivery and presentation of solutions",
"similarity": 0.3814
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.4154,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "persistence and data modeling",
"sentence": "\u00b7 Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer.",
"similarity": 0.4343
},
{
"kra_text": "persistence and data modeling",
"sentence": "\u00b7 Have worked on multiple TBs of data volume from ingestion to consumption",
"similarity": 0.4028
},
{
"kra_text": "service contract collaboration",
"sentence": "\u00b7 Engage in the delivery and presentation of solutions",
"similarity": 0.3733
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.4034,
"slug": "java-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 6,
"matched_skills": [
"AWS",
"Apache Spark",
"Hadoop",
"Python",
"SQL",
"Spark Streaming"
],
"role_id": 2,
"score": 0.3,
"slug": "data-engineer",
"total_count": 20
},
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"API",
"AWS",
"Python"
],
"role_id": 15,
"score": 0.15,
"slug": "full-stack-engineer",
"total_count": 20
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"Python"
],
"role_id": 3,
"score": 0.1,
"slug": "ml-engineer",
"total_count": 20
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"Python"
],
"role_id": 5,
"score": 0.1,
"slug": "cybersecurity-engineer",
"total_count": 20
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"AWS",
"Python"
],
"role_id": 1,
"score": 0.1,
"slug": "backend-engineer",
"total_count": 20
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"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": 1.0,
"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": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.30 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 192,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 9972,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "MapReduce",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9973,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "HDFS",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9974,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Oozie",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9975,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Lake",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9976,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Big Data",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9977,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Ingestion",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9978,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Transformation",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9979,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Consumption",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 9980,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Mining",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 9981,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Business Intelligence",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"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": 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": 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": 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"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 319,
"existing_alias_text": "Spark Streaming",
"input_term": "Spark Streaming",
"matched_canonical": {
"category_id": 5,
"display_name": "Spark Streaming",
"id": 121,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "spark-streaming",
"sub_category_id": 94,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 4198,
"existing_alias_text": "Hive",
"input_term": "Hive",
"matched_canonical": {
"category_id": 3,
"display_name": "Hive",
"id": 2754,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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{
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],
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},
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{
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{
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},
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"display_name": "Kotlin Backend Developer",
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"slug": "ml-engineer",
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},
{
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{
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{
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"rationale": null,
"role_archetype": "Engineering",
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{
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]
},
{
"dimension": {
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"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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"source": "db"
},
"input_skill": "AWS",
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"roles_from_db": [
{
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{
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
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"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
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},
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"roles_from_db": [
{
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},
{
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{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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"input_skill": "Spark",
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{
"display_name": "Data Engineer",
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]
},
{
"dimension": {
"difficulty_hint": "well_known",
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},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Mining",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-mining",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Business Intelligence",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "business-intelligence",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Relational Databases",
"alias_type": "CANONICAL",
"id": 1988,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 37,
"display_name": "Relational Databases",
"id": 1345,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "relational-databases",
"sub_category_id": 1018,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Relational Databases",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Relational Databases",
"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": [
"MapReduce",
"HDFS",
"Oozie",
"Big Data",
"Data Ingestion",
"Data Consumption",
"Data Mining",
"Business Intelligence"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.30 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Spark Streaming",
"tag": "in_db"
},
{
"skill": "Hive",
"tag": "in_db"
},
{
"skill": "MapReduce",
"tag": "new"
},
{
"skill": "HDFS",
"tag": "new"
},
{
"skill": "HBase",
"tag": "in_db"
},
{
"skill": "Oozie",
"tag": "new"
},
{
"skill": "API",
"tag": "in_db"
},
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "Lakehouse",
"tag": "in_db"
},
{
"skill": "Big Data",
"tag": "new"
},
{
"skill": "Data Ingestion",
"tag": "new"
},
{
"skill": "Data Transformation",
"tag": "in_db"
},
{
"skill": "Data Consumption",
"tag": "new"
},
{
"skill": "Data Mining",
"tag": "new"
},
{
"skill": "Business Intelligence",
"tag": "new"
},
{
"skill": "Relational Databases",
"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": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 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": "SQL",
"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": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 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": "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": "AWS",
"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": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"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"
},
"dimension_id": 211,
"input_skill": "AWS",
"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": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"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"
},
"dimension_id": 131,
"input_skill": "AWS",
"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"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"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"
},
"dimension_id": 64,
"input_skill": "AWS",
"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"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"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": "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,
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{
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{
"display_name": "Native Mobile Developer",
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{
"display_name": "React Native Developer",
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{
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{
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},
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{
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],
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{
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},
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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{
"display_name": "Angular Frontend Developer",
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{
"display_name": "Frontend Developer",
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{
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{
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{
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{
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{
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{
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"matched_chosen_role": false,
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{
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{
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},
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"role_dimension_saved": false,
"roles_from_db": [],
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}
],
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.