Pipeline run
b0c1edb4-ff3d-478c-aa20-e942622f535d
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 JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
We Have Immediate Opening - Data Warehouse Consultant (ETL). Should have hands-on experience on ETL, SSIS, SSRS 4 + Years of experience mandatory. Remote Working - Anywhere from India. Job Description: Position: Data Warehouse Consultant (ETL) Type: Fulltime Year of Experience: 4 - 8 Years Joining: Immediate Joining Preferred(Negotiable up to 15 Days). Headline: We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions & supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional & cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques. Principal Accountabilities: Requirement gathering & understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule & productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms Skills Required: Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL & PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI Work Location & Culture: Full-time position Remote Working - Anywhere from India Flexible working hours Interested candidates; Share your Latest CV on: hr@dataentrega.com & Apply on: -https://www.dataentrega.com/careers/
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
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
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
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Snowflake (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Cloud Platform
- Vendor
- Snowflake Inc.
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 113
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Dimensional modeling (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Data Modeling Concept
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in analytics/data-warehouse JDs and BI roles; star/snowflake schema terms appear frequently in job postings and vendor docs for Snowflake/BigQuery/Redshift.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 10
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Modeling and Schema Design Catalog dimension db id 26
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Modeling and Schema Design
data-modeling-and-schema-design
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Warehousing
- 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
- 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
- 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
- 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 Warehousing
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- RDBMS (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database Management System
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RDBMS is a core requirement in many job descriptions across backend, data, and DBA roles; PostgreSQL, MySQL, and SQL Server remain standard enterprise stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 1023
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- PL/SQL (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Procedural Sql Language
- Vendor
- Oracle Corporation
- License
- proprietary
- Year introduced
- 1990
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PL/SQL appears frequently in Oracle-focused job postings and remains a standard skill for Oracle database development and maintenance; it is not sunset or replaced by a newer successor.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 1173
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
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
- Power BI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2015
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | 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
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
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 |
|---|---|---|---|---|---|---|
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | 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) | |
| Google BigQuery | new |
Cloud Data Warehouses
cloud-data-warehouses
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Dimensional Modeling | in_db |
Data Modeling and Schema Design
data-modeling-and-schema-design
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| RDBMS | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PL/SQL | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Power BI | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | WhereScape RED | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | WhereScape 3D | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Teradata | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure SQL | type=Databases subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Amazon | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Vault | type=Data Warehousing subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehouse Automation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Data Warehousing subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SSRS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Scheduling Tool | type=Data Engineering Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| dimension_skill_link_proposed | Google BigQuery ↔ Cloud Data Warehouses | |
| role_dimension_link_proposed | Data Engineer ↔ Cloud Data Warehouses |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Data Entrega",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 8,
"min": 4,
"raw": "4 - 8 Years"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "Data Warehouse Consultant (ETL)",
"role_aliases": [
"Data Warehouse Consultant",
"ETL Consultant",
"Data Integration Consultant"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Headline",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are looking for an",
"last_5_words": "modeling techniques."
},
"text": "We are looking for an ELT/Data Integration Consultant. The consultant will join a Professional Services Team who are responsible for developing Data Warehouse Automation solutions \u0026 supporting various customers to provide technical expertise on an ELT Automation tool. The role is focused on automation tools WhereScape RED and 3D integrated with various traditional \u0026 cloud platforms like Oracle, Teradata, Snowflake, Azure SQL, Hive, Amazon, Google Big query, etc. applied in the areas of Dimensional as well as Data Vault modeling techniques.",
"word_count": 64
},
{
"bullet_count": 0,
"heading": "Principal Accountabilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Requirement gathering \u0026 understanding",
"last_5_words": "to Cloud platforms"
},
"text": "Requirement gathering \u0026 understanding of client Data Warehouse needs Provide Data Warehouse Automation solutions Develop, schedule \u0026 productionize the ELT Conduct Proof of Concepts (POC) for clients in assistance with Solutions Architect Migration of Data Warehouse systems from traditional RDBMS platforms to Cloud platforms",
"word_count": 38
},
{
"bullet_count": 0,
"heading": "Skills Required",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Must have excellent command over",
"last_5_words": "and self-explore Knowledge of Power BI"
},
"text": "Must have excellent command over communication (Written and Verbal) Knowledge of Data Warehousing modeling techniques- Kimball- Dimensional modeling or Data Vault 2.0 Should have extensively worked in any of the ETL/ELT tools Good understanding of any Database Architectures/RDBMS Excellent SQL \u0026 PL/SQL skills Should have worked on SSIS and SSRS Experience in any scheduling tool Good problem-solving capability and self-explore Knowledge of Power BI",
"word_count": 66
}
],
"urls": [
{
"type": "careers",
"url": "https://www.dataentrega.com/careers/"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "WhereScape RED"
},
{
"is_primary": true,
"skill_name": "WhereScape 3D"
},
{
"is_primary": true,
"skill_name": "Oracle"
},
{
"is_primary": true,
"skill_name": "Teradata"
},
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": true,
"skill_name": "Azure SQL"
},
{
"is_primary": true,
"skill_name": "Hive"
},
{
"is_primary": true,
"skill_name": "Amazon"
},
{
"is_primary": true,
"skill_name": "Google BigQuery"
},
{
"is_primary": true,
"skill_name": "Dimensional Modeling"
},
{
"is_primary": true,
"skill_name": "Data Vault"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "ELT"
},
{
"is_primary": true,
"skill_name": "Data Warehouse Automation"
},
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "RDBMS"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "PL/SQL"
},
{
"is_primary": true,
"skill_name": "SSIS"
},
{
"is_primary": true,
"skill_name": "SSRS"
},
{
"is_primary": false,
"skill_name": "Power BI"
},
{
"is_primary": false,
"skill_name": "Scheduling Tool"
}
],
"jd_role": {
"display_name": "Data Warehouse Consultant (ETL)",
"rationale": null,
"role_aliases": [
"Data Warehouse Consultant",
"ETL Consultant",
"Data Integration Consultant"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Data Entrega",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": 8,
"min": 4,
"raw": "4 - 8 Years"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "Data Warehouse Consultant (ETL)",
"role_aliases": [
"Data Warehouse Consultant",
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API 2 — extract-details
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"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "PL/SQL",
"alias_type": "CANONICAL",
"id": 2513,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "PL/SQL",
"id": 1567,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "pl-sql",
"sub_category_id": 1173,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"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": "PL/SQL",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "PL/SQL",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "SSIS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ssis",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "SSRS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ssrs",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Power BI",
"alias_type": "CANONICAL",
"id": 360,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Power BI",
"id": 151,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "power-bi",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"input_skill": "Power BI",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Power BI",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Scheduling Tool",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "SHORT_LIVED",
"version_strategy": "VERSIONED",
"volatility": "FAST"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "scheduling-tool",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"WhereScape RED",
"WhereScape 3D",
"Oracle",
"Teradata",
"Azure SQL",
"Amazon",
"Data Vault",
"ETL",
"ELT",
"Data Warehouse Automation",
"Data Warehousing",
"SSIS",
"SSRS",
"Scheduling Tool"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ELT/data integration, data warehouse automation, migration, scheduling, and ETL/ELT tooling, which best matches a Data Engineer role.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "WhereScape RED",
"tag": "new"
},
{
"skill": "WhereScape 3D",
"tag": "new"
},
{
"skill": "Oracle",
"tag": "new"
},
{
"skill": "Teradata",
"tag": "new"
},
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "Azure SQL",
"tag": "new"
},
{
"skill": "Hive",
"tag": "in_db"
},
{
"skill": "Amazon",
"tag": "new"
},
{
"skill": "Google BigQuery",
"tag": "in_db"
},
{
"skill": "Dimensional Modeling",
"tag": "in_db"
},
{
"skill": "Data Vault",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "ELT",
"tag": "new"
},
{
"skill": "Data Warehouse Automation",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "RDBMS",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "PL/SQL",
"tag": "in_db"
},
{
"skill": "SSIS",
"tag": "new"
},
{
"skill": "SSRS",
"tag": "new"
},
{
"skill": "Power BI",
"tag": "in_db"
},
{
"skill": "Scheduling Tool",
"tag": "new"
}
],
"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": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Snowflake",
"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": 105,
"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
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
"id": 22,
"rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
"slug": "cloud-data-warehouses",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Google BigQuery",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Modeling and Schema Design",
"id": 26,
"rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
"slug": "data-modeling-and-schema-design",
"source": "db"
},
"dimension_id": 26,
"input_skill": "Dimensional Modeling",
"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": 125,
"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": "RDBMS",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1355,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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 \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"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": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "PL/SQL",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1567,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"dimension_id": 31,
"input_skill": "Power BI",
"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": 151,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 1
},
"planner_output": null,
"run_id": "b0c1edb4-ff3d-478c-aa20-e942622f535d"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.