Pipeline run
9bd37876-38fa-4481-9ba5-d79d3dc251b3
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 devops-engineer 0.15 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Greetings from TCS !! Role : BigData Developer Experience Range : 3 to 8 years Location : Chennai/ Hyderabad/ Bangalore/ Pune/ Kolkata Skills : BigData, Pyspark, Scala, Hadoop, Hive Must Have : • Hands on development experience in programming languages such as SCALA using Maven, Apache Spark Frameworks and Unix Shell scripting • Should be comfortable with Unix File system as well as HDFS commands • Should have worked on query languages such as Oracle SQL, Hive SQL, Spark SQL, Impala, HBase DB • Should be flexible • Should have good communication and customer management skills Roles and Responsibilities : • Design high quality deliverables adhering to business requirement with defined standards and design principles, patterns • Develop and maintain highly scalable, high performance Data transformation applications using Apache Spark framework • Develop/Integrate the code adhering to CI/CD, using Spark Framework in Scala • Provide solutions to Big data problems dealing with huge volumes of data using Spark based data transformation solutions , Hive
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
- Scala (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- EPFL
- License
- apache_2
- Year introduced
- 2004
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.
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)
-
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
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) |
Aliases — catalog
- Maven (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Build Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2004
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Maven remains a standard Java build tool, appearing in many Java/JVM job descriptions and widely used in enterprise CI/CD pipelines alongside Gradle.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 358
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Build and Dependency Management Catalog dimension db id 286
Library dimension (catalog)
Roles linked in library: Java Backend Developer
-
Build and Packaging Tooling Catalog dimension db id 149
Library dimension (catalog)
Roles linked in library: DevOps Engineer, Ionic Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Build and Dependency Management
build-and-dependency-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Build and Packaging Tooling
build-and-packaging-tooling
|
✓ | — | 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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Programming 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
- Operating Systems
- Sub-category
- general
- Skill nature
- PLATFORM
- 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
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Oracle Database (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- Oracle Corporation
- License
- proprietary
- Year introduced
- 1979
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Oracle Database appears in many enterprise job postings and remains a standard RDBMS in large regulated environments; Oracle continues active vendor support and releases, indicating broad market demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Relational Database Design
relational-database-design
|
— | — |
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
- 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
- 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
- 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) |
Aliases — catalog
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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) |
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 |
|---|---|---|---|---|---|---|
| Scala | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Scala | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Maven | in_db |
Build and Dependency Management
build-and-dependency-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Maven | in_db |
Build and Packaging Tooling
build-and-packaging-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Apache Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Oracle SQL | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Oracle SQL | new |
Relational Database Design
relational-database-design
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| HBase | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hive | in_db |
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Unix Shell Scripting | type=Programming Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Unix | type=Operating Systems subtype=general nature=PLATFORM lifespan=EVERGREEN | |
| canonical_skill_proposed | HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Hive SQL | type=Query Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Spark SQL | type=Query Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Impala | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Oracle SQL ↔ React Frontend Development | |
| dimension_skill_link_proposed | Oracle SQL ↔ Relational Database Design |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "TCS",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 8,
"min": 3,
"raw": "3 to 8 years"
},
"job_locations": [
{
"aliases": [],
"city": "Chennai",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [],
"city": "Hyderabad",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [],
"city": "Pune",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [
"Calcutta"
],
"city": "Kolkata",
"country": null,
"state": null,
"work_mode": null
}
],
"role": "BigData Developer",
"role_aliases": [
"Big Data Engineer",
"Big Data Developer",
"Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Must Have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Hands on development experience",
"last_5_words": "communication and customer management skills"
},
"text": "\u2022 Hands on development experience in programming languages such as SCALA using Maven, Apache Spark Frameworks and Unix Shell scripting \n\u2022 Should be comfortable with Unix File system as well as HDFS commands \n\u2022 Should have worked on query languages such as Oracle SQL, Hive SQL, Spark SQL, Impala, HBase DB \n\u2022 Should be flexible \n\u2022 Should have good communication and customer management skills",
"word_count": 51
},
{
"bullet_count": 4,
"heading": "Roles and Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design high quality deliverables",
"last_5_words": "data transformation solutions , Hive"
},
"text": "\u2022 Design high quality deliverables adhering to business requirement with defined standards and design principles, patterns\n\u2022 Develop and maintain highly scalable, high performance Data transformation applications using Apache Spark framework\n\u2022 Develop/Integrate the code adhering to CI/CD, using Spark Framework in Scala\n\u2022 Provide solutions to Big data problems dealing with huge volumes of data using Spark based data transformation solutions , Hive",
"word_count": 64
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Scala"
},
{
"is_primary": true,
"skill_name": "Maven"
},
{
"is_primary": true,
"skill_name": "Apache Spark"
},
{
"is_primary": true,
"skill_name": "Unix Shell Scripting"
},
{
"is_primary": true,
"skill_name": "Unix"
},
{
"is_primary": true,
"skill_name": "HDFS"
},
{
"is_primary": true,
"skill_name": "Oracle SQL"
},
{
"is_primary": true,
"skill_name": "Hive SQL"
},
{
"is_primary": true,
"skill_name": "Spark SQL"
},
{
"is_primary": true,
"skill_name": "Impala"
},
{
"is_primary": true,
"skill_name": "HBase"
},
{
"is_primary": true,
"skill_name": "CI/CD"
},
{
"is_primary": true,
"skill_name": "Hive"
}
],
"jd_role": {
"display_name": "BigData Developer",
"rationale": null,
"role_aliases": [
"Big Data Engineer",
"Big Data Developer",
"Data Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "TCS",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 8,
"min": 3,
"raw": "3 to 8 years"
},
"job_locations": [
{
"aliases": [],
"city": "Chennai",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [],
"city": "Hyderabad",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [],
"city": "Pune",
"country": null,
"state": null,
"work_mode": null
},
{
"aliases": [
"Calcutta"
],
"city": "Kolkata",
"country": null,
"state": null,
"work_mode": null
}
],
"role": "BigData Developer",
"role_aliases": [
"Big Data Engineer",
"Big Data Developer",
"Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Must Have",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Hands on development experience",
"last_5_words": "communication and customer management skills"
},
"text": "\u2022 Hands on development experience in programming languages such as SCALA using Maven, Apache Spark Frameworks and Unix Shell scripting \n\u2022 Should be comfortable with Unix File system as well as HDFS commands \n\u2022 Should have worked on query languages such as Oracle SQL, Hive SQL, Spark SQL, Impala, HBase DB \n\u2022 Should be flexible \n\u2022 Should have good communication and customer management skills",
"word_count": 51
},
{
"bullet_count": 4,
"heading": "Roles and Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design high quality deliverables",
"last_5_words": "data transformation solutions , Hive"
},
"text": "\u2022 Design high quality deliverables adhering to business requirement with defined standards and design principles, patterns\n\u2022 Develop and maintain highly scalable, high performance Data transformation applications using Apache Spark framework\n\u2022 Develop/Integrate the code adhering to CI/CD, using Spark Framework in Scala\n\u2022 Provide solutions to Big data problems dealing with huge volumes of data using Spark based data transformation solutions , Hive",
"word_count": 64
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "9bd37876-38fa-4481-9ba5-d79d3dc251b3",
"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": "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": "Develop and maintain highly scalable, high performance Data transformation applications using Apache Spark framework",
"similarity": 0.6959
},
{
"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": "Provide solutions to Big data problems dealing with huge volumes of data using Spark based data transformation solutions , Hive",
"similarity": 0.5986
},
{
"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": "Develop/Integrate the code adhering to CI/CD, using Spark Framework in Scala",
"similarity": 0.5424
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6123,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Should have worked on query languages such as Oracle SQL, Hive SQL, Spark SQL, Impala, HBase DB",
"similarity": 0.5044
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Design high quality deliverables adhering to business requirement with defined standards and design principles, patterns",
"similarity": 0.4794
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Develop and maintain highly scalable, high performance Data transformation applications using Apache Spark framework",
"similarity": 0.4336
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4725,
"slug": "full-stack-engineer",
"total_count": null
},
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{
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{
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],
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{
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]
},
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"case": "A",
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"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top devops-engineer 0.15 does not contradict",
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"centroid_updated": true,
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],
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}
}
API 2 — extract-details
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"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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"matched_via": "embedding_alias"
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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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"is_primary": false,
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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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},
{
"dimension": {
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"source": "db"
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},
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},
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},
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},
{
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"input_skill": "Impala",
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},
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},
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},
{
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{
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"id": 2011,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
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"is_extractable": true,
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},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"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"
},
"input_skill": "HBase",
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{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
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}
]
}
],
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},
{
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{
"alias_text": "CI/CD",
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"id": 1826,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_also_category": false,
"is_extractable": true,
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},
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{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "CI/CD",
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"display_name": "DevOps Engineer",
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"slug": "devops-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
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},
"input_skill": "CI/CD",
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{
"display_name": "ML Engineer",
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"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "CI/CD",
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Hive",
"alias_type": "CANONICAL",
"id": 4198,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Hive",
"id": 2754,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "hive",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Local Persistence and Offline Behavior",
"id": 85,
"rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
"slug": "local-persistence-and-offline-behavior",
"source": "db"
},
"input_skill": "Hive",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Android Developer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Flutter Developer",
"id": 74,
"rationale": null,
"role_archetype": "Engineering",
"slug": "flutter-developer",
"source": "db"
},
{
"display_name": "Hybrid Mobile Developer",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
},
{
"display_name": "Native Mobile Developer",
"id": 75,
"rationale": null,
"role_archetype": "Engineering",
"slug": "native-mobile-developer",
"source": "db"
},
{
"display_name": "React Native Developer",
"id": 73,
"rationale": null,
"role_archetype": "Engineering",
"slug": "react-native-developer",
"source": "db"
},
{
"display_name": "iOS Developer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "ios-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hive",
"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": [
"Unix Shell Scripting",
"Unix",
"HDFS",
"Hive SQL",
"Spark SQL",
"Impala"
]
}
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 devops-engineer 0.15 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Scala",
"tag": "in_db"
},
{
"skill": "Maven",
"tag": "in_db"
},
{
"skill": "Apache Spark",
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