Pipeline run
56356888-a1d6-4792-a50a-f50178cb30b3
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 centers on building and orchestrating modern data warehouse and ETL pipelines, triaging pipeline issues, and working with Snowflake/Databricks/ADF/Airflow, which best matches Data 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
Company: Vidpro Consultency Services Website: Visit Website Business Type: Startup Company Type: Product & Service Business Model: B2B Funding Stage: Series D+ Salary Range: ₹ 6-16 Lacs PA Job Description This is a permanent role with Valued Client of Vidpro Consulting. Primary Skills Python, pyspark, Azure Data Factory, snowflake, snowpipe, snowsql, Snowsight, Snowpark, ETL and SQL. • Snowpro certified is plus Primary Roles And Responsibilities • Developing Modern Data Warehouse solutions using Snowflake, Databricks and ADF. • Ability to provide solutions that are forward-thinking in data engineering and analytics space • Collaborate with DW/BI leads to understand new ETL pipeline development requirements. • Triage issues to find gaps in existing pipelines and fix the issues • Work with business to understand the need in the reporting layer and develop a data model to fulfill reporting needs • Help joiner team members to resolve issues and technical challenges. • Drive technical discussions with client architect and team members • Orchestrate the data pipelines in the scheduler via Airflow Skills And Qualifications • Bachelor's and/or master’s degree in computer science or equivalent experience. • Must have total 3+ yrs. of IT experience and 3+ years' experience in Data warehouse/ETL projects. • Expertise in Snowflake security, Snowflake SQL and designing/implementing other Snowflake objects. • Hands-on experience with Snowflake utilities, SnowSQL, Snowpipe, Snowsight and Snowflake connectors. • Deep understanding of Star and Snowflake dimensional modeling. • Strong knowledge of Data Management principles • Good understanding of Databricks Data & AI platform and Databricks Delta Lake Architecture • Should have hands-on experience in SQL and Spark (PySpark) • Experience in building ETL / data warehouse transformation processes • Experience with Open Source non-relational / NoSQL data repositories (incl. MongoDB, Cassandra, Neo4J) • Experience working with structured and unstructured data including imaging & geospatial data. • Experience working in a Dev/Ops environment with tools such as Terraform, CircleCI, GIT. • Proficiency in RDBMS, complex SQL, PL/SQL, Unix Shell Scripting, performance tuning, troubleshooting and Query Optimization. • Databricks Certified Data Engineer Associate/Professional Certification (Desirable). • Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects • Should have experience working in Agile methodology • Strong verbal and written communication skills. • Strong analytical and problem-solving skills with a high attention to detail.
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
- 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
- Databricks (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Analytics Platform
- Vendor
- Databricks, Inc.
- License
- other_open
- Year introduced
- 2013
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 911
- 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
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- domain modeling (CANONICAL) primary
- Domain Modeling (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Domain Modeling
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 2831
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Application Architecture Patterns Catalog dimension db id 293
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Python Backend Developer
-
Service Architecture and Design Patterns Catalog dimension db id 18
Library dimension (catalog)
Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Application Architecture Patterns
application-architecture-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Workflow Orchestration Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.95
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 130
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Workflow Orchestration for ML Pipelines Catalog dimension db id 54
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | 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 |
|---|---|---|---|---|---|---|
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Databricks | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Modeling | new |
Application Architecture Patterns
application-architecture-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Modeling | new |
Service Architecture and Design Patterns
service-architecture-and-design-patterns
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Airflow | in_db |
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | ADF | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Modeling ↔ Application Architecture Patterns | |
| dimension_skill_link_proposed | Data Modeling ↔ Service Architecture and Design Patterns |
nano JD Parser — gpt-4.1-nano click to toggle
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [
"Snowpro certified",
"Databricks Certified Data Engineer Associate/Professional Certification (Desirable)"
],
"company_name": "Vidpro Consultency Services",
"ctc": {
"currency": "INR",
"max": 16,
"min": 6,
"period": "annual",
"raw": "\u20b9 6-16 Lacs PA"
},
"domain": {
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"BPO",
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],
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}
],
"experience": {
"max": null,
"min": 3,
"raw": "Must have total 3+ yrs. of IT experience and 3+ years\u0027 experience in Data warehouse/ETL projects."
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Data Engineer",
"ETL Developer",
"Data Warehouse Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Primary Roles And Responsibilities",
"heading_was_present": true,
"source_marker": {
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"last_5_words": "in the scheduler via Airflow"
},
"text": "\u2022 Developing Modern Data Warehouse solutions using Snowflake, Databricks and ADF.\n\u2022 Ability to provide solutions that are forward-thinking in data engineering and analytics space\n\u2022 Collaborate with DW/BI leads to understand new ETL pipeline development requirements.\n\u2022 Triage issues to find gaps in existing pipelines and fix the issues\n\u2022 Work with business to understand the need in the reporting layer and develop a data model to fulfill reporting needs\n\u2022 Help joiner team members to resolve issues and technical challenges.\n\u2022 Drive technical discussions with client architect and team members\n\u2022 Orchestrate the data pipelines in the scheduler via Airflow",
"word_count": 92
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": true,
"skill_name": "Databricks"
},
{
"is_primary": true,
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},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Data Modeling"
},
{
"is_primary": true,
"skill_name": "Airflow"
}
],
"jd_role": {
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"rationale": null,
"role_aliases": [
"Data Engineer",
"ETL Developer",
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],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
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"about_company": null,
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],
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"source_marker": {
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"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "56356888-a1d6-4792-a50a-f50178cb30b3",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
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"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
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},
{
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"matched_count": null,
"matched_skills": null,
"role_id": 144,
"score": 1.0,
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],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
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"similarity": 0.6125
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with DW/BI leads to understand new ETL pipeline development requirements.",
"similarity": 0.6005
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work with business to understand the need in the reporting layer and develop a data model to fulfill reporting needs",
"similarity": 0.5995
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6042,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
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"sentence": "Drive technical discussions with client architect and team members",
"similarity": 0.553
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Help joiner team members to resolve issues and technical challenges.",
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},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Collaborate with DW/BI leads to understand new ETL pipeline development requirements.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5048,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
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"sentence": "Drive technical discussions with client architect and team members",
"similarity": 0.594
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Help joiner team members to resolve issues and technical challenges.",
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},
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
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}
],
"matched_count": null,
"matched_skills": null,
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"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
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"sentence": "Triage issues to find gaps in existing pipelines and fix the issues",
"similarity": 0.5338
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Collaborate with DW/BI leads to understand new ETL pipeline development requirements.",
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{
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],
"matched_count": null,
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"role_id": 10,
"score": 0.4846,
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},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
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},
{
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},
{
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],
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],
"skill_match_roles": [
{
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"matched_skills": [
"Snowflake"
],
"role_id": 2,
"score": 0.1667,
"slug": "data-engineer",
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},
{
"display_name": "ML Engineer",
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"matched_skills": [
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],
"role_id": 3,
"score": 0.1667,
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},
{
"display_name": "MLOps Engineer",
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"matched_count": 1,
"matched_skills": [
"Airflow"
],
"role_id": 16,
"score": 0.1667,
"slug": "ml-ops-engineer",
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}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.98,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Modern Data Warehouse Engineering",
"ETL Pipeline Development",
"Data Pipeline Troubleshooting",
"Reporting Data Modeling",
"Workflow Orchestration",
"Technical Collaboration"
],
"matched_kras": [
"Developing Modern Data Warehouse solutions",
"Collaborate with DW/BI leads",
"understand new ETL pipeline development requirements",
"triage issues to find gaps in existing pipelines",
"fix the issues",
"develop a data model to fulfill reporting needs",
"help joiner team members to resolve issues",
"drive technical discussions with client architect and team members",
"orchestrate the data pipelines in the scheduler via Airflow"
],
"matched_skills": [
"Snowflake",
"Databricks",
"ADF",
"ETL pipeline",
"Airflow",
"data model",
"reporting layer",
"DW/BI",
"client architect"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and orchestrating modern data warehouse and ETL pipelines, triaging pipeline issues, and working with Snowflake/Databricks/ADF/Airflow, which best matches Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 383,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 18180,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ADF",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 18181,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 18182,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Modeling",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 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": 1838,
"existing_alias_text": "Databricks",
"input_term": "Databricks",
"matched_canonical": {
"category_id": 9,
"display_name": "Databricks",
"id": 1202,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "databricks",
"sub_category_id": 911,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 5644,
"existing_alias_text": "Domain Modeling",
"input_term": "Data Modeling",
"matched_canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 526,
"existing_alias_text": "Airflow",
"input_term": "Airflow",
"matched_canonical": {
"category_id": 13,
"display_name": "Airflow",
"id": 265,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "airflow",
"sub_category_id": 130,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "Data Engineer",
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"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and orchestrating modern data warehouse and ETL pipelines, triaging pipeline issues, and working with Snowflake/Databricks/ADF/Airflow, which best matches Data Engineer.",
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"slug": "data-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"slug": "cloud-data-warehouses",
"source": "db"
},
"input_skill": "Snowflake",
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"roles_from_db": [
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"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": "Databricks",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
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"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
"slug": "application-architecture-patterns",
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},
"input_skill": "Data Modeling",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
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"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
"id": 54,
"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"input_skill": "Airflow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_final_skills": [
"Snowflake",
"Databricks",
"ADF",
"ETL",
"Data Modeling",
"Airflow"
],
"input_llm_skills": [
"Snowflake",
"Databricks",
"ADF",
"ETL",
"Data Modeling",
"Airflow"
],
"new_aliases_persisted": 0,
"run_id": "56356888-a1d6-4792-a50a-f50178cb30b3",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Snowflake",
"alias_type": "CANONICAL",
"id": 299,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Snowflake",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "snowflake",
"sub_category_id": 113,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"slug": "cloud-data-warehouses",
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},
"input_skill": "Snowflake",
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"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Snowflake",
"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": "Databricks",
"alias_type": "CANONICAL",
"id": 1838,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Databricks",
"id": 1202,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "databricks",
"sub_category_id": 911,
"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": "Databricks",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Databricks",
"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": "ADF",
"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": "adf",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ETL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"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": "etl",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "domain modeling",
"alias_type": "CANONICAL",
"id": 3675,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Domain Modeling",
"alias_type": "CANONICAL",
"id": 5644,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
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"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
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"source": "db"
},
"input_skill": "Data Modeling",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
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},
{
"display_name": "Python Backend Developer",
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"role_archetype": "Engineering",
"slug": "python-backend-developer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
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"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Data Modeling",
"matched_via": "embedding_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": "Airflow",
"alias_type": "CANONICAL",
"id": 526,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow 2",
"alias_type": "VERSION",
"id": 2477,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow-2",
"alias_type": "VERSION",
"id": 2478,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow2",
"alias_type": "VERSION",
"id": 2476,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "airflow2.x",
"alias_type": "VERSION",
"id": 2479,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "apache airflow 2",
"alias_type": "VERSION",
"id": 2480,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Airflow",
"id": 265,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "airflow",
"sub_category_id": 130,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration for ML Pipelines",
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"rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
"slug": "workflow-orchestration-for-ml-pipelines",
"source": "db"
},
"input_skill": "Airflow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
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}
]
}
],
"input_skill": "Airflow",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"ADF",
"ETL"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on building and orchestrating modern data warehouse and ETL pipelines, triaging pipeline issues, and working with Snowflake/Databricks/ADF/Airflow, which best matches Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "Databricks",
"tag": "in_db"
},
{
"skill": "ADF",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Data Modeling",
"tag": "in_db"
},
{
"skill": "Airflow",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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",
"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": "React Frontend Development",
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"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": "Databricks",
"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": 1202,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
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"rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
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