Pipeline run
0ff2002c-5f95-4558-a9e8-432257524c42
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
• Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation. • Implement best practices and drive adoption around coding, design, qualit…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
ETL / ELT Developer
domain · Data Engineering & Analytics CASE DOMAINslug: etl-elt-developer · id: 50 · source: db
Domain=Data Engineering & Analytics; The JD is centered on ETL development and DataStage-based data integration, metadata migration, and support of data processing systems, which best matches an ETL/ELT Developer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto “ Building Success Together® ,” Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today’s world, with immersive and seamless experiences across digital and physical channels. What You'll Do • Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation. • Implement best practices and drive adoption around coding, design, quality, and performance. • Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise. What You Know • The overall experience of 7 to 10 years in the Information Technology. • Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices. • Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc.) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices. • Experience with both normalized and dimensional data models, hands-on knowledge of other data integration techniques such as database replication, change data capture (CDC) etc. and familiarity of SOA and ESB technologies and patterns. Demonstrable Skills: • Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables. • Complete work within a reasonable time frame under the supervision of a manager or team lead. • Plan and manage all aspects of the support function. • Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation. • Strong knowledge in project management practices and ability to document processes and procedures as needed. • Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision • Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables Education • Bachelor’s / Master’s degree in specific technical fields like computer science, math, statistics preferred, or equivalent practical experience. Benefits • In addition to competitive salaries and benefits packages, Nisum India offers its employees some unique and fun extras: • Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis. We support our team to excel in their field. • Parental Medical Insurance - Nisum believes our team is the heart of our business and we want to make sure to take care of the heart of theirs. We offer opt-in parental medical insurance in addition to our medical benefits. • Activities -From the Nisum Premier League's cricket tournaments to hosted Hack-a-thon, Nisum employees can participate in a variety of team building activities such as skits, dances performance in addition to festival celebrations. • Free Meals - Free snacks and dinner is provided on a daily basis, in addition to subsidized lunch. Nisum is an Equal Opportunity Employer and we are proud of our ongoing efforts to foster diversity and inclusion in the workplace.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- ETL Tools
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Change data capture (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Data Capture Methodology
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CDC is broadly adopted in data engineering; it appears in many JDs for Kafka/Debezium/ETL roles and is a standard pattern for near-real-time replication and sync.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 102
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Batch Ingestion and Replication Catalog dimension db id 29
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Batch Ingestion and Replication
batch-ingestion-and-replication
|
✓ | — | 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
- Architecture Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Architecture Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Dimensional modeling (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Data Modeling Concept
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in analytics/data-warehouse JDs and BI roles; star/snowflake schema terms appear frequently in job postings and vendor docs for Snowflake/BigQuery/Redshift.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 10
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Data Modeling and Schema Design Catalog dimension db id 26
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Data Modeling and Schema Design
data-modeling-and-schema-design
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- data mapping (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Data Mapping
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data mapping appears broadly in ETL/ELT, integration, and MDM job descriptions across BI and cloud data stacks; vendors like Informatica, dbt, and Azure Data Factory all center it as a core capability.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3239
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
External System Integrations Catalog dimension db id 14
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Drupal Dev, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
External System Integrations
external-system-integrations
|
✓ | — | 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
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Soft Skills
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- DevOps Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
|---|---|---|---|---|---|---|
| Change Data Capture | in_db |
Batch Ingestion and Replication
batch-ingestion-and-replication
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Dimensional Data Model | new |
Data Modeling and Schema Design
data-modeling-and-schema-design
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Mapping | in_db |
External System Integrations
external-system-integrations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | IBM DataStage | type=Data Engineering Tools subtype=ETL Tools nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Integration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Database Replication | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SOA | type=Architecture Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ESB | type=Architecture Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Normalized Data Model | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Processing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Project Management | type=Soft Skills subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Automation | type=DevOps Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Dimensional Data Model ↔ Data Modeling and Schema Design |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Nisum is a leading global",
"last_5_words": "digital and physical channels."
},
"text": "Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto \u201c Building Success Together\u00ae ,\u201d Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today\u2019s world, with immersive and seamless experiences across digital and physical channels.",
"word_count": 84
},
"certifications": [],
"company_name": "Nisum",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Bachelor\u2019s / Master\u2019s degree in specific technical fields like computer science, math, statistics preferred, or equivalent practical experience.",
"requirement": "preferred"
}
],
"experience": {
"max": 10,
"min": 7,
"raw": "7 to 10 years in the Information Technology"
},
"job_locations": [],
"role": "Business Intelligence Developer",
"role_aliases": [
"BI Developer",
"ETL Developer",
"Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 3,
"heading": "What You\u0027ll Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Business Intelligence developer in",
"last_5_words": "respect, open communication, and cross-functional expertise."
},
"text": "\u2022 Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation.\n\u2022 Implement best practices and drive adoption around coding, design, quality, and performance.\n\u2022 Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise.",
"word_count": 45
},
{
"bullet_count": 4,
"heading": "What You Know",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 The overall experience of 7",
"last_5_words": "and ESB technologies and patterns."
},
"text": "\u2022 The overall experience of 7 to 10 years in the Information Technology.\n\u2022 Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices.\n\u2022 Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc.) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices.\n\u2022 Experience with both normalized and dimensional data models, hands-on knowledge of other data integration techniques such as database replication, change data capture (CDC) etc. and familiarity of SOA and ESB technologies and patterns.",
"word_count": 104
},
{
"bullet_count": 7,
"heading": "Demonstrable Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Strong analytical skills with",
"last_5_words": "tasks and deliverables."
},
"text": "\u2022 Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables.\n\u2022 Complete work within a reasonable time frame under the supervision of a manager or team lead.\n\u2022 Plan and manage all aspects of the support function.\n\u2022 Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation.\n\u2022 Strong knowledge in project management practices and ability to document processes and procedures as needed.\n\u2022 Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision\n\u2022 Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables.",
"word_count": 118
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "IBM DataStage"
},
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "Data Integration"
},
{
"is_primary": true,
"skill_name": "Database Replication"
},
{
"is_primary": true,
"skill_name": "Change Data Capture"
},
{
"is_primary": true,
"skill_name": "SOA"
},
{
"is_primary": true,
"skill_name": "ESB"
},
{
"is_primary": true,
"skill_name": "Normalized Data Model"
},
{
"is_primary": true,
"skill_name": "Dimensional Data Model"
},
{
"is_primary": true,
"skill_name": "Data Mapping"
},
{
"is_primary": true,
"skill_name": "Data Processing"
},
{
"is_primary": false,
"skill_name": "Project Management"
},
{
"is_primary": false,
"skill_name": "Automation"
}
],
"jd_role": {
"display_name": "Business Intelligence Developer",
"rationale": null,
"role_aliases": [
"BI Developer",
"ETL Developer",
"Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
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"last_5_words": "digital and physical channels."
},
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"word_count": 84
},
"certifications": [],
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"ctc": null,
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"experience": {
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"source_marker": {
"first_5_words": "\u2022 Business Intelligence developer in",
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},
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{
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},
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"word_count": 104
},
{
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"source_marker": {
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},
"text": "\u2022 Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables.\n\u2022 Complete work within a reasonable time frame under the supervision of a manager or team lead.\n\u2022 Plan and manage all aspects of the support function.\n\u2022 Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation.\n\u2022 Strong knowledge in project management practices and ability to document processes and procedures as needed.\n\u2022 Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision\n\u2022 Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables.",
"word_count": 118
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "0ff2002c-5f95-4558-a9e8-432257524c42",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"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": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices.",
"similarity": 0.5511
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc. ) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices.",
"similarity": 0.5319
},
{
"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": "Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation.",
"similarity": 0.5248
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5359,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise.",
"similarity": 0.599
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision",
"similarity": 0.5012
},
{
"kra_text": "optimize responsiveness and performance",
"sentence": "Implement best practices and drive adoption around coding, design, quality, and performance.",
"similarity": 0.4828
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5277,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Implement best practices and drive adoption around coding, design, quality, and performance.",
"similarity": 0.5752
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise.",
"similarity": 0.5309
},
{
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],
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}
}
API 2 — extract-details
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"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "soa",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ESB",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Architecture Concepts",
"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": "esb",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Normalized Data Model",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"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": "normalized-data-model",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Dimensional modeling",
"alias_type": "CANONICAL",
"id": 329,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Dimensional modeling",
"id": 125,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "dimensional-modeling",
"sub_category_id": 10,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Modeling and Schema Design",
"id": 26,
"rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
"slug": "data-modeling-and-schema-design",
"source": "db"
},
"input_skill": "Dimensional Data Model",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Dimensional Data Model",
"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": "data mapping",
"alias_type": "CANONICAL",
"id": 3824,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "data mapping",
"id": 2487,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "data-mapping",
"sub_category_id": 3239,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "External System Integrations",
"id": 14,
"rationale": "Connecting backend services to third-party APIs and internal enterprise systems. This includes client libraries, webhooks, retries, data mapping, and integration failure handling.",
"slug": "external-system-integrations",
"source": "db"
},
"input_skill": "Data Mapping",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-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"
}
]
}
],
"input_skill": "Data Mapping",
"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": "Data Processing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"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": "data-processing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Project Management",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Soft Skills",
"skill_nature": "PRACTICE",
"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": "project-management",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Automation",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "DevOps Tools",
"skill_nature": "PRACTICE",
"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": "automation",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"ETL",
"IBM DataStage",
"Data Warehousing",
"Data Integration",
"Database Replication",
"SOA",
"ESB",
"Normalized Data Model",
"Data Processing",
"Project Management",
"Automation"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "ETL / ELT Developer",
"id": 50,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ETL development and DataStage-based data integration, metadata migration, and support of data processing systems, which best matches an ETL/ELT Developer.",
"role_archetype": "Data",
"slug": "etl-elt-developer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "IBM DataStage",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "Data Integration",
"tag": "new"
},
{
"skill": "Database Replication",
"tag": "new"
},
{
"skill": "Change Data Capture",
"tag": "in_db"
},
{
"skill": "SOA",
"tag": "new"
},
{
"skill": "ESB",
"tag": "new"
},
{
"skill": "Normalized Data Model",
"tag": "new"
},
{
"skill": "Dimensional Data Model",
"tag": "in_db"
},
{
"skill": "Data Mapping",
"tag": "in_db"
},
{
"skill": "Data Processing",
"tag": "new"
},
{
"skill": "Project Management",
"tag": "new"
},
{
"skill": "Automation",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Batch Ingestion and Replication",
"id": 29,
"rationale": "Moving data from source systems into landing zones or warehouses on batch schedules. Covers file ingestion, CDC-style replication, incremental loads, and source-to-target synchronization.",
"slug": "batch-ingestion-and-replication",
"source": "db"
},
"dimension_id": 29,
"input_skill": "Change Data Capture",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 140,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Modeling and Schema Design",
"id": 26,
"rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
"slug": "data-modeling-and-schema-design",
"source": "db"
},
"dimension_id": 26,
"input_skill": "Dimensional Data Model",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 50,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "External System Integrations",
"id": 14,
"rationale": "Connecting backend services to third-party APIs and internal enterprise systems. This includes client libraries, webhooks, retries, data mapping, and integration failure handling.",
"slug": "external-system-integrations",
"source": "db"
},
"dimension_id": 14,
"input_skill": "Data Mapping",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-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"
}
],
"skill_dimension_saved": true,
"skill_id": 2487,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 1
},
"planner_output": null,
"run_id": "0ff2002c-5f95-4558-a9e8-432257524c42"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.