Pipeline run
fd8e8aea-616f-47a5-a76a-3080c75d53e6
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
- Build and maintain dbt models that power our metric layer. - Collaborate with analysts and BI teams on KPI definitions. - Own data quality tests and lineage in dbt. - Optimize warehouse query perfor…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Analytics Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: analytics-engineer · id: 142 · source: db
Domain=Data Engineering & Analytics; The JD explicitly mentions building and maintaining dbt models, collaborating with analysts and BI teams, and optimizing Snowflake queries — all core responsibilities of an Analytics Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Analytics Engineer Responsibilities: - Build and maintain dbt models that power our metric layer. - Collaborate with analysts and BI teams on KPI definitions. - Own data quality tests and lineage in dbt. - Optimize warehouse query performance in Snowflake. - Document business logic and data contracts. Skills: dbt, Snowflake, SQL, Looker, BI, metric layer.
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
- dbt (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Analytics Engineering Framework
- Vendor
- dbt Labs
- License
- apache_2
- Year introduced
- 2016
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: dbt appears in many analytics engineer and data platform job descriptions, and its GitHub repo has strong adoption signals with widespread ecosystem support from major cloud/data vendors.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 89
- 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 skipped (dimension not under chosen role) |
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 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
- ISO/IEC
- License
- unknown
- Year introduced
- 1986
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL is a hiring-pipeline staple across data, backend, and analytics roles; it appears in a very high volume of job descriptions and remains the standard query language for relational databases.
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 skipped (dimension not under chosen role) |
Aliases — catalog
- Looker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Google Cloud
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.
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 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 |
|---|---|---|---|---|---|---|
| dbt | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | — | 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 skipped (dimension not under chosen role) | |
| Looker | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"ai_kras": [],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [],
"role": "Analytics Engineer",
"role_aliases": [
{
"name": "Analytics Engineer",
"reasoning": "generalized form of the picked role",
"relation": "synonym"
},
{
"name": "Data Engineer",
"reasoning": "role involves data modeling and analytics",
"relation": "adjacent"
},
{
"name": "Business Intelligence Engineer",
"reasoning": "role involves BI tools and metrics",
"relation": "adjacent"
}
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Build and maintain dbt models",
"last_5_words": "business logic and data contracts."
},
"text": "- Build and maintain dbt models that power our metric layer.\n- Collaborate with analysts and BI teams on KPI definitions.\n- Own data quality tests and lineage in dbt.\n- Optimize warehouse query performance in Snowflake.\n- Document business logic and data contracts.",
"word_count": 41
},
{
"bullet_count": 0,
"heading": "Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "dbt, Snowflake, SQL, Looker,",
"last_5_words": "BI, metric layer."
},
"text": "dbt, Snowflake, SQL, Looker, BI, metric layer.",
"word_count": 8
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "dbt"
},
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Looker"
}
],
"jd_role": {
"display_name": "Analytics Engineer",
"rationale": null,
"role_aliases": [
"Analytics Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"ai_kras": [],
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [],
"role": "Analytics Engineer",
"role_aliases": [
{
"name": "Analytics Engineer",
"reasoning": "generalized form of the picked role",
"relation": "synonym"
},
{
"name": "Data Engineer",
"reasoning": "role involves data modeling and analytics",
"relation": "adjacent"
},
{
"name": "Business Intelligence Engineer",
"reasoning": "role involves BI tools and metrics",
"relation": "adjacent"
}
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Build and maintain dbt models",
"last_5_words": "business logic and data contracts."
},
"text": "- Build and maintain dbt models that power our metric layer.\n- Collaborate with analysts and BI teams on KPI definitions.\n- Own data quality tests and lineage in dbt.\n- Optimize warehouse query performance in Snowflake.\n- Document business logic and data contracts.",
"word_count": 41
},
{
"bullet_count": 0,
"heading": "Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "dbt, Snowflake, SQL, Looker,",
"last_5_words": "BI, metric layer."
},
"text": "dbt, Snowflake, SQL, Looker, BI, metric layer.",
"word_count": 8
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "fd8e8aea-616f-47a5-a76a-3080c75d53e6",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
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"matched_skills": null,
"role_id": 2,
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},
{
"display_name": "Analytics Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 142,
"score": 1.0,
"slug": "analytics-engineer",
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}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Optimize warehouse query performance in Snowflake.",
"similarity": 0.6756
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with analysts and BI teams on KPI definitions.",
"similarity": 0.5599
},
{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
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"slug": "data-engineer",
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},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "Server-side business logic implementation",
"sentence": "Document business logic and data contracts.",
"similarity": 0.6417
},
{
"kra_text": "backend performance tuning",
"sentence": "Optimize warehouse query performance in Snowflake.",
"similarity": 0.4528
},
{
"kra_text": "persistence and data modeling",
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"similarity": 0.4467
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.5137,
"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "PHP Backend Developer",
"kra_matches": [
{
"kra_text": "Server-side business logic implementation",
"sentence": "Document business logic and data contracts.",
"similarity": 0.6417
},
{
"kra_text": "automated backend regression checks",
"sentence": "Own data quality tests and lineage in dbt.",
"similarity": 0.475
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Optimize warehouse query performance in Snowflake.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 86,
"score": 0.4897,
"slug": "php-backend-developer",
"total_count": null
},
{
"display_name": "Python Backend Developer",
"kra_matches": [
{
"kra_text": "Implement server-side business logic",
"sentence": "Document business logic and data contracts.",
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},
{
"kra_text": "Write backend-focused automated checks",
"sentence": "Own data quality tests and lineage in dbt.",
"similarity": 0.4293
},
{
"kra_text": "Build service and external integrations",
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"similarity": 0.3698
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 80,
"score": 0.4823,
"slug": "python-backend-developer",
"total_count": null
},
{
"display_name": "Kotlin Backend Developer",
"kra_matches": [
{
"kra_text": "Backend business logic implementation",
"sentence": "Document business logic and data contracts.",
"similarity": 0.6721
},
{
"kra_text": "data access and persistence",
"sentence": "Own data quality tests and lineage in dbt.",
"similarity": 0.3765
},
{
"kra_text": "backend refactoring and maintenance",
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],
"matched_count": null,
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"role_id": 84,
"score": 0.4719,
"slug": "kotlin-server-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"Looker",
"SQL",
"Snowflake",
"dbt"
],
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": 4
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.25,
"slug": "pega-developer",
"total_count": 4
},
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 121,
"score": 0.25,
"slug": "engineering-manager",
"total_count": 4
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Analytics Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 142,
"score": 0.95,
"slug": "analytics-engineer",
"total_count": null
},
"confidence": 0.95,
"consensus_evidence": {},
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Data Modeling with dbt",
"Data Quality and Lineage",
"Warehouse Query Performance Optimization",
"Business Logic Documentation",
"Data Contracts"
],
"matched_kras": [
"Build and maintain dbt models that power our metric layer.",
"Collaborate with analysts and BI teams on KPI definitions.",
"Own data quality tests and lineage in dbt.",
"Optimize warehouse query performance in Snowflake.",
"Document business logic and data contracts."
],
"matched_skills": [
"dbt",
"Snowflake",
"SQL",
"Looker",
"BI"
],
"new_role_display_name": null,
"new_role_domain": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD explicitly mentions building and maintaining dbt models, collaborating with analysts and BI teams, and optimizing Snowflake queries \u2014 all core responsibilities of an Analytics Engineer.",
"role_aliases": [
{
"name": "Data Engineer",
"reason": "The JD involves building data models and optimizing warehouse performance, which overlaps with data engineering responsibilities.",
"relation": "synonym"
},
{
"name": "BI Developer",
"reason": "The JD mentions collaboration with BI teams and tools like Looker, aligning with BI development work.",
"relation": "synonym"
}
],
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 2,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 2049,
"r_and_r_preview": "- Build and maintain dbt models that power our metric layer.\n- Collaborate with analysts and BI teams on KPI definitions.\n- Own data quality tests and lineage in dbt.\n- Optimize warehouse query perfor",
"role_display_name": "Analytics Engineer",
"role_slug": "analytics-engineer",
"status": "pending"
},
"new_skills_attached": [],
"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": 309,
"existing_alias_text": "dbt",
"input_term": "dbt",
"matched_canonical": {
"category_id": 5,
"display_name": "dbt",
"id": 115,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "dbt",
"sub_category_id": 89,
"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": 299,
"existing_alias_text": "Snowflake",
"input_term": "Snowflake",
"matched_canonical": {
"category_id": 9,
"display_name": "Snowflake",
"id": 105,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "snowflake",
"sub_category_id": 113,
"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": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
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"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 361,
"existing_alias_text": "Looker",
"input_term": "Looker",
"matched_canonical": {
"category_id": 9,
"display_name": "Looker",
"id": 152,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "looker",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
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"slug": "data-engineer",
"source": "db"
},
{
"display_name": "Pega Developer",
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"slug": "pega-developer",
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},
{
"display_name": "Engineering Manager",
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"slug": "engineering-manager",
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}
],
"chosen_role": {
"display_name": "Analytics Engineer",
"id": 142,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD explicitly mentions building and maintaining dbt models, collaborating with analysts and BI teams, and optimizing Snowflake queries \u2014 all core responsibilities of an Analytics Engineer.",
"role_archetype": null,
"slug": "analytics-engineer",
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},
"dimensions": [
{
"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"
},
"input_skill": "dbt",
"llm_role": null,
"roles_from_db": [
{
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}
]
},
{
"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"
},
"input_skill": "Snowflake",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"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,
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{
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"slug": "pega-developer",
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}
]
},
{
"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",
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},
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{
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}
]
},
{
"dimension": {
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"slug": "programming-languages-for-data-work",
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},
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{
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}
]
},
{
"dimension": {
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"slug": "bi-and-visualization-tools",
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},
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{
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}
]
}
],
"input_final_skills": [
"dbt",
"Snowflake",
"SQL",
"Looker"
],
"input_llm_skills": [
"dbt",
"Snowflake",
"SQL",
"Looker"
],
"new_aliases_persisted": 0,
"run_id": "fd8e8aea-616f-47a5-a76a-3080c75d53e6",
"skills_detail": [
{
"aliases_in_db": [
{
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}
],
"canonical": {
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
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"slug": "etl-and-elt-tooling",
"source": "db"
},
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{
"display_name": "Data Engineer",
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}
]
}
],
"input_skill": "dbt",
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"new_skill_meta": null,
"source_tag": "db",
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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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},
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}
],
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"new_skill_meta": null,
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},
{
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{
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}
],
"canonical": {
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"typical_lifespan": "EVERGREEN",
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},
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{
"dimension": {
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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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"new_skill_meta": null,
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},
{
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{
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],
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
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{
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],
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}
],
"unmatched_skills": []
}
API 3 — final-role-output
{
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"role_archetype": null,
"slug": "analytics-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "dbt",
"tag": "in_db"
},
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Looker",
"tag": "in_db"
}
],
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"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
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{
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],
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},
{
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
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{
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],
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},
{
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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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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},
{
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},
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{
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],
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}
],
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},
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.