Pipeline run
62bbcb2c-6242-4535-b735-7962b917683b
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
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The role centers on managing and validating ETL/data onboarding work with databases, cloud, and Hadoop/Hive exposure, which best matches a Data Engineer leadership function.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Role Summary: This job involves managing a team which onboards data from source transactional database into data warehouse, tracking working, reporting & highlighting status, risks & Issues. It also involves extensive collaboration with other team members (India & USA) to follow a plan, and own deliverables. Essential Responsibilities: • Manage data onboarding team (around 6 to 8 members) • Assign tasks to team members and track status • Take accountability of work (timely completion & quality) and drive to closure • Co-ordinate with team members & stakeholders in US • Validate / verify work by verifying data in target system and job execution status • Document status, dependencies, risks & issues in status report and circulate to stakeholders • Be a single point of contact to provide status and updates to leadership Required Skills: • Bachelor’s degree from a reputed university • 12 – 15 years overall experience with 2+ years experience in leading a small to medium team • Very good knowledge in data (querying tables) and database concepts • Excellent communication and positive attitude • Experience working / leading ETL project • Basic knowledge in cloud with exposure in one or more projects • Good people management skills • Attitude to take ownership and drive / encourage / motivate team towards successful completion of work Good to have skills : • Hands on experience in any cloud • Knowledge in US Healthcare domain • Knowledge in Agile • Experience in Big data technologies like Hadoop, Hive, etc
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 |
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
- Cloud (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Cloud Computing
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Cloud is a hiring-pipeline staple: AWS, Azure, and GCP appear in a large share of modern infrastructure JDs, and major vendors continue expanding cloud services rather than sunsetting them.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1177
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 367
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- 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
- 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 |
|---|---|---|---|---|---|---|
| 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 | |
| Cloud | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | 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 | |
| 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 | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Hive",
"Agile",
"Hadoop",
"Cloud",
"Role"
],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Any Discipline",
"raw": "Bachelor\u2019s degree from a reputed university",
"requirement": "required"
}
],
"experience": {
"max": 15,
"min": 12,
"raw": "12 \u2013 15 years overall experience with 2+ years experience in leading a small to medium team"
},
"job_locations": [],
"role": "Data Onboarding Team Manager",
"role_aliases": [
"Data Manager",
"Team Lead",
"Data Team Manager"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 7,
"heading": "Essential Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Manage data onboarding team",
"last_5_words": "status and updates to leadership"
},
"text": "\u2022 Manage data onboarding team (around 6 to 8 members) \n \u2022 Assign tasks to team members and track status \n \u2022 Take accountability of work (timely completion \u0026 quality) and drive to closure \n \u2022 Co-ordinate with team members \u0026 stakeholders in US \n \u2022 Validate / verify work by verifying data in target system and job execution status \n \u2022 Document status, dependencies, risks \u0026 issues in status report and circulate to stakeholders \n \u2022 Be a single point of contact to provide status and updates to leadership",
"word_count": 83
},
{
"bullet_count": 8,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u2019s degree from a",
"last_5_words": "successful completion of work"
},
"text": "\u2022 Bachelor\u2019s degree from a reputed university \n \u2022 12 \u2013 15 years overall experience with 2+ years experience in leading a small to medium team \n \u2022 Very good knowledge in data (querying tables) and database concepts \n \u2022 Excellent communication and positive attitude \n \u2022 Experience working / leading ETL project \n \u2022 Basic knowledge in cloud with exposure in one or more projects \n \u2022 Good people management skills \n \u2022 Attitude to take ownership and drive / encourage / motivate team towards successful completion of work",
"word_count": 88
},
{
"bullet_count": 4,
"heading": "Good to have skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Hands on experience in",
"last_5_words": "technologies like Hadoop, Hive, etc"
},
"text": "\u2022 Hands on experience in any cloud \n \u2022 Knowledge in US Healthcare domain \n \u2022 Knowledge in Agile \n \u2022 Experience in Big data technologies like Hadoop, Hive, etc",
"word_count": 34
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": false,
"skill_name": "Cloud"
},
{
"is_primary": false,
"skill_name": "Agile"
},
{
"is_primary": false,
"skill_name": "Hadoop"
},
{
"is_primary": false,
"skill_name": "Hive"
}
],
"jd_role": {
"display_name": "Data Onboarding Team Manager",
"rationale": null,
"role_aliases": [
"Data Manager",
"Team Lead",
"Data Team Manager"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Hive",
"Agile",
"Hadoop",
"Cloud",
"Role"
],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Any Discipline",
"raw": "Bachelor\u2019s degree from a reputed university",
"requirement": "required"
}
],
"experience": {
"max": 15,
"min": 12,
"raw": "12 \u2013 15 years overall experience with 2+ years experience in leading a small to medium team"
},
"job_locations": [],
"role": "Data Onboarding Team Manager",
"role_aliases": [
"Data Manager",
"Team Lead",
"Data Team Manager"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 7,
"heading": "Essential Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Manage data onboarding team",
"last_5_words": "status and updates to leadership"
},
"text": "\u2022 Manage data onboarding team (around 6 to 8 members) \n \u2022 Assign tasks to team members and track status \n \u2022 Take accountability of work (timely completion \u0026 quality) and drive to closure \n \u2022 Co-ordinate with team members \u0026 stakeholders in US \n \u2022 Validate / verify work by verifying data in target system and job execution status \n \u2022 Document status, dependencies, risks \u0026 issues in status report and circulate to stakeholders \n \u2022 Be a single point of contact to provide status and updates to leadership",
"word_count": 83
},
{
"bullet_count": 8,
"heading": "Required Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u2019s degree from a",
"last_5_words": "successful completion of work"
},
"text": "\u2022 Bachelor\u2019s degree from a reputed university \n \u2022 12 \u2013 15 years overall experience with 2+ years experience in leading a small to medium team \n \u2022 Very good knowledge in data (querying tables) and database concepts \n \u2022 Excellent communication and positive attitude \n \u2022 Experience working / leading ETL project \n \u2022 Basic knowledge in cloud with exposure in one or more projects \n \u2022 Good people management skills \n \u2022 Attitude to take ownership and drive / encourage / motivate team towards successful completion of work",
"word_count": 88
},
{
"bullet_count": 4,
"heading": "Good to have skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Hands on experience in",
"last_5_words": "technologies like Hadoop, Hive, etc"
},
"text": "\u2022 Hands on experience in any cloud \n \u2022 Knowledge in US Healthcare domain \n \u2022 Knowledge in Agile \n \u2022 Experience in Big data technologies like Hadoop, Hive, etc",
"word_count": 34
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "62bbcb2c-6242-4535-b735-7962b917683b",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"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": "Experience working / leading ETL project",
"similarity": 0.4751
},
{
"kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
"sentence": "Validate / verify work by verifying data in target system and job execution status",
"similarity": 0.4602
},
{
"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": "Very good knowledge in data (querying tables) and database concepts",
"similarity": 0.4495
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.4616,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Co-ordinate with team members \u0026 stakeholders in US",
"similarity": 0.5082
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Manage data onboarding team (around 6 to 8 members)",
"similarity": 0.418
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Assign tasks to team members and track status",
"similarity": 0.4111
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.4458,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Validate / verify work by verifying data in target system and job execution status",
"similarity": 0.5042
},
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Co-ordinate with team members \u0026 stakeholders in US",
"similarity": 0.4107
},
{
"kra_text": "Maintains ML platform runbooks, on-call escalation playbooks, and deployment procedure documentation for production operations teams.",
"sentence": "Be a single point of contact to provide status and updates to leadership",
"similarity": 0.3826
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4325,
"slug": "ml-ops-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": "Very good knowledge in data (querying tables) and database concepts",
"similarity": 0.4853
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Co-ordinate with team members \u0026 stakeholders in US",
"similarity": 0.438
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Experience working / leading ETL project",
"similarity": 0.3635
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4289,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": ".NET Backend Developer",
"kra_matches": [
{
"kra_text": "validation and error handling",
"sentence": "Validate / verify work by verifying data in target system and job execution status",
"similarity": 0.4684
},
{
"kra_text": "defect investigation and resolution",
"sentence": "Take accountability of work (timely completion \u0026 quality) and drive to closure",
"similarity": 0.4034
},
{
"kra_text": "data access and persistence behavior",
"sentence": "Very good knowledge in data (querying tables) and database concepts",
"similarity": 0.3914
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 83,
"score": 0.4211,
"slug": "dotnet-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 2,
"score": 0.5,
"slug": "data-engineer",
"total_count": 2
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.5,
"slug": "pega-developer",
"total_count": 2
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.95,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.95,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Data Onboarding Management",
"ETL Project Leadership",
"Team and Stakeholder Coordination",
"Data Validation and Quality Assurance",
"Cloud and Big Data Exposure",
"People Management"
],
"matched_kras": [
"Manage data onboarding team",
"Assign tasks to team members and track status",
"Take accountability of work",
"Drive to closure",
"Co-ordinate with team members \u0026 stakeholders in US",
"Validate / verify work by verifying data in target system",
"Document status, dependencies, risks \u0026 issues",
"Provide status and updates to leadership"
],
"matched_skills": [
"data",
"querying tables",
"database concepts",
"ETL",
"cloud",
"Hadoop",
"Hive",
"Agile",
"US Healthcare"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The role centers on managing and validating ETL/data onboarding work with databases, cloud, and Hadoop/Hive exposure, which best matches a Data Engineer leadership function.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 217,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 11038,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"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": 2518,
"existing_alias_text": "Cloud",
"input_term": "Cloud",
"matched_canonical": {
"category_id": 37,
"display_name": "Cloud",
"id": 1572,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "cloud",
"sub_category_id": 1177,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 868,
"existing_alias_text": "Agile",
"input_term": "Agile",
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"is_extractable": true,
"skill_nature": "TOOL",
"slug": "hive",
"sub_category_id": 2242,
"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": [
"ETL"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The role centers on managing and validating ETL/data onboarding work with databases, cloud, and Hadoop/Hive exposure, which best matches a Data Engineer leadership function.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Cloud",
"tag": "in_db"
},
{
"skill": "Agile",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Hive",
"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 Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "Cloud",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1572,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Agile",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 520,
"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": "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"
},
"dimension_id": 85,
"input_skill": "Hive",
"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": "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"
}
],
"skill_dimension_saved": true,
"skill_id": 2754,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "62bbcb2c-6242-4535-b735-7962b917683b"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.