Pipeline run
c5daec39-df63-4c8d-89ee-3eaaf2486cae
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
Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI. Partner with business users to understand reporting needs, define requirements, and translate them into eff…
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
The role of Data Engineer aligns well with the primary skills related to data warehousing and BI tools.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Title: BI Developer As a BI Developer, you will play a key role in designing, developing, and maintaining reporting and analytics solutions that support operational and strategic business needs. You will be responsible for building intuitive dashboards, analyzing data trends, defining KPIs, and contributing to data-driven process improvement initiatives. Responsibilities: - Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI. - Partner with business users to understand reporting needs, define requirements, and translate them into effective BI solutions. - Write, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis. - Perform deep-dive analyses to identify trends, anomalies, and improvement opportunities. - Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery). - Establish KPI definitions in partnership with finance, ops, and product stakeholders. - Maintain a self-serve BI culture: build governed dashboards, train end-users. Requirements: - 4+ years of BI development experience. - Strong SQL, Tableau, Power BI. - Experience with at least one cloud data warehouse (Snowflake/Redshift/BigQuery). - Strong communication skills.
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
- Tableau (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Tableau Software
- License
- proprietary
- Year introduced
- 2003
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform 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 |
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 |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- 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 |
Aliases — catalog
- Amazon Redshift (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in data/analytics job descriptions and widely used as AWS’s managed warehouse; strong vendor adoption and steady JD volume signal broad market demand.
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
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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 |
|---|---|---|---|---|---|---|
| Tableau | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Power BI | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| 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 | |
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Amazon Redshift | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| BigQuery | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 4,
"raw": "4+ years of BI development experience"
},
"job_locations": [],
"role": "BI Developer",
"role_aliases": [
"Business Intelligence Developer",
"BI Engineer",
"Data Analyst"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 7,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design, develop, and maintain interactive",
"last_5_words": "governed dashboards, train end-users."
},
"text": "Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI.\nPartner with business users to understand reporting needs, define requirements, and translate them into effective BI solutions.\nWrite, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis.\nPerform deep-dive analyses to identify trends, anomalies, and improvement opportunities.\nBuild and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).\nEstablish KPI definitions in partnership with finance, ops, and product stakeholders.\nMaintain a self-serve BI culture: build governed dashboards, train end-users.",
"word_count": 104
},
{
"bullet_count": 4,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "4+ years of BI development",
"last_5_words": "communication skills."
},
"text": "4+ years of BI development experience.\nStrong SQL, Tableau, Power BI.\nExperience with at least one cloud data warehouse (Snowflake/Redshift/BigQuery).\nStrong communication skills.",
"word_count": 36
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Tableau"
},
{
"is_primary": true,
"skill_name": "Power BI"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": true,
"skill_name": "Amazon Redshift"
},
{
"is_primary": true,
"skill_name": "BigQuery"
}
],
"jd_role": {
"display_name": "BI Developer",
"rationale": null,
"role_aliases": [
"Business Intelligence Developer",
"BI Engineer",
"Data Analyst"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "Other"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 4,
"raw": "4+ years of BI development experience"
},
"job_locations": [],
"role": "BI Developer",
"role_aliases": [
"Business Intelligence Developer",
"BI Engineer",
"Data Analyst"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 7,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design, develop, and maintain interactive",
"last_5_words": "governed dashboards, train end-users."
},
"text": "Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI.\nPartner with business users to understand reporting needs, define requirements, and translate them into effective BI solutions.\nWrite, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis.\nPerform deep-dive analyses to identify trends, anomalies, and improvement opportunities.\nBuild and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).\nEstablish KPI definitions in partnership with finance, ops, and product stakeholders.\nMaintain a self-serve BI culture: build governed dashboards, train end-users.",
"word_count": 104
},
{
"bullet_count": 4,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "4+ years of BI development",
"last_5_words": "communication skills."
},
"text": "4+ years of BI development experience.\nStrong SQL, Tableau, Power BI.\nExperience with at least one cloud data warehouse (Snowflake/Redshift/BigQuery).\nStrong communication skills.",
"word_count": 36
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "c5daec39-df63-4c8d-89ee-3eaaf2486cae",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Analyst",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 143,
"score": 1.0,
"slug": "data-analyst",
"total_count": null
},
{
"display_name": "BI Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 147,
"score": 1.0,
"slug": "bi-developer",
"total_count": null
}
],
"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": "Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).",
"similarity": 0.6494
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Partner with business users to understand reporting needs, define requirements, and translate them into effective BI solutions.",
"similarity": 0.5873
},
{
"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": "Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI.",
"similarity": 0.5828
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6065,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Write, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis.",
"similarity": 0.5693
},
{
"kra_text": "Debugs full-stack issues that span frontend rendering, API behavior, database queries, and infrastructure configuration to identify root causes.",
"sentence": "Perform deep-dive analyses to identify trends, anomalies, and improvement opportunities.",
"similarity": 0.4696
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).",
"similarity": 0.4658
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5016,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Perform deep-dive analyses to identify trends, anomalies, and improvement opportunities.",
"similarity": 0.4988
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).",
"similarity": 0.4769
},
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Partner with business users to understand reporting needs, define requirements, and translate them into effective BI solutions.",
"similarity": 0.4529
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4762,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Writes database access logic including SQL queries, ORM mappings, stored procedures, and migration scripts for relational databases like PostgreSQL and MySQL.",
"sentence": "Write, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis.",
"similarity": 0.4965
},
{
"kra_text": "Investigates and resolves production incidents, API bugs, and service degradation through root cause analysis, hotfixes, and post-mortems.",
"sentence": "Perform deep-dive analyses to identify trends, anomalies, and improvement opportunities.",
"similarity": 0.4828
},
{
"kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
"sentence": "Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).",
"similarity": 0.4064
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.4619,
"slug": "backend-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
"sentence": "Build and document data pipelines feeding the BI layer (Snowflake / Redshift / BigQuery).",
"similarity": 0.47
},
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Perform deep-dive analyses to identify trends, anomalies, and improvement opportunities.",
"similarity": 0.4275
},
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Write, optimize, and maintain SQL queries for data extraction, transformation, validation, and analysis.",
"similarity": 0.4157
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4378,
"slug": "ml-ops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 6,
"matched_skills": [
"Amazon Redshift",
"BigQuery",
"Power BI",
"SQL",
"Snowflake",
"Tableau"
],
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": 6
},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"SQL"
],
"role_id": 24,
"score": 0.1667,
"slug": "pega-developer",
"total_count": 6
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "BI Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 147,
"score": 0.99,
"slug": "bi-developer",
"total_count": null
},
"confidence": 0.99,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"BI Dashboard and Report Development",
"Business Stakeholder Collaboration",
"SQL-Based Data Analysis",
"Cloud Data Warehouse Integration",
"KPI Definition and Reporting",
"Self-Serve BI Enablement"
],
"matched_kras": [
"Design, develop, and maintain interactive dashboards and reports",
"Partner with business users to understand reporting needs",
"Write, optimize, and maintain SQL queries",
"Perform deep-dive analyses to identify trends, anomalies",
"Build and document data pipelines feeding the BI layer",
"Establish KPI definitions in partnership with stakeholders",
"Maintain a self-serve BI culture"
],
"matched_skills": [
"Tableau",
"Power BI",
"SQL",
"Snowflake",
"Redshift",
"BigQuery"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on dashboard/report development, BI tooling, SQL, KPI definitions, and self-serve BI enablement, which best matches BI Developer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 1,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 124,
"r_and_r_preview": "Design, develop, and maintain interactive dashboards and reports using Tableau and Power BI.\nPartner with business users to understand reporting needs, define requirements, and translate them into eff",
"role_display_name": "BI Developer",
"role_slug": "bi-developer",
"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": 359,
"existing_alias_text": "Tableau",
"input_term": "Tableau",
"matched_canonical": {
"category_id": 9,
"display_name": "Tableau",
"id": 150,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "tableau",
"sub_category_id": 111,
"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": 360,
"existing_alias_text": "Power BI",
"input_term": "Power BI",
"matched_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"
},
"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,
"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": 299,
"existing_alias_text": "Snowflake",
"input_term": "Snowflake",
"matched_canonical": {
"category_id": 9,
"display_name": "Snowflake",
"id": 105,
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"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": 301,
"existing_alias_text": "Amazon Redshift",
"input_term": "Amazon Redshift",
"matched_canonical": {
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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": 300,
"existing_alias_text": "BigQuery",
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"matched_canonical": {
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},
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}
],
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{
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{
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"slug": "pega-developer",
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}
],
"chosen_role": {
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"rationale": "The role of Data Engineer aligns well with the primary skills related to data warehousing and BI tools.",
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"slug": "data-engineer",
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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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"slug": "data-engineer",
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}
]
},
{
"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",
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}
]
},
{
"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.",
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},
"input_skill": "SQL",
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"roles_from_db": [
{
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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",
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"roles_from_db": [
{
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"role_archetype": null,
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}
]
},
{
"dimension": {
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"source": "db"
},
"input_skill": "Amazon Redshift",
"llm_role": null,
"roles_from_db": [
{
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"role_archetype": null,
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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.",
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},
"input_skill": "BigQuery",
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"roles_from_db": [
{
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}
]
}
],
"input_final_skills": [
"Tableau",
"Power BI",
"SQL",
"Snowflake",
"Amazon Redshift",
"BigQuery"
],
"input_llm_skills": [
"Tableau",
"Power BI",
"SQL",
"Snowflake",
"Amazon Redshift",
"BigQuery"
],
"new_aliases_persisted": 0,
"run_id": "c5daec39-df63-4c8d-89ee-3eaaf2486cae",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Tableau",
"alias_type": "CANONICAL",
"id": 359,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Tableau",
"id": 150,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "tableau",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
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"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",
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},
"input_skill": "Tableau",
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"roles_from_db": [
{
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"slug": "data-engineer",
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}
]
}
],
"input_skill": "Tableau",
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"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
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},
{
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{
"alias_text": "Power BI",
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}
],
"canonical": {
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"display_name": "Power BI",
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},
"dimensions": [
{
"dimension": {
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},
"input_skill": "Power BI",
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"roles_from_db": [
{
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"slug": "data-engineer",
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}
]
}
],
"input_skill": "Power BI",
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"new_alias_persisted": false,
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},
{
"aliases_in_db": [
{
"alias_text": "SQL",
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"id": 271,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "SQL",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
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"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",
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"roles_from_db": [
{
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"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
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"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.",
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},
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"roles_from_db": [
{
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}
]
}
],
"input_skill": "SQL",
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"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Snowflake",
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"is_primary": true,
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}
],
"canonical": {
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"display_name": "Snowflake",
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"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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",
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"roles_from_db": [
{
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}
]
}
],
"input_skill": "Snowflake",
"matched_via": "alias",
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"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
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{
"alias_text": "Amazon Redshift",
"alias_type": "CANONICAL",
"id": 301,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Amazon Redshift",
"id": 107,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "amazon-redshift",
"sub_category_id": 118,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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": "Amazon Redshift",
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"roles_from_db": [
{
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}
]
}
],
"input_skill": "Amazon Redshift",
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"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
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},
{
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{
"alias_text": "BigQuery",
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"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
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"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
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},
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{
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},
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{
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}
]
}
],
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}
],
"unmatched_skills": []
}
API 3 — final-role-output
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"rationale": "The role of Data Engineer aligns well with the primary skills related to data warehousing and BI tools.",
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"slug": "data-engineer",
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},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
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"tag": "in_db"
},
{
"skill": "Power BI",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "Amazon Redshift",
"tag": "in_db"
},
{
"skill": "BigQuery",
"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": [
{
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"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
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"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.",
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},
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"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
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{
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}
],
"skill_dimension_saved": true,
"skill_id": 150,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
"dimension": {
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"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.",
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},
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
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"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",
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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,
"roles_from_db": [
{
"display_name": "Pega Developer",
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"role_archetype": null,
"slug": "pega-developer",
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}
],
"skill_dimension_saved": true,
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"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",
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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",
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"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",
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},
{
"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.",
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"source": "db"
},
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"input_skill": "Amazon Redshift",
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
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"skill_tag": "in_db",
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},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Data Warehouses",
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"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": "BigQuery",
"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": 106,
"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": "c5daec39-df63-4c8d-89ee-3eaaf2486cae"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.