Pipeline run
c3c8f394-09e1-4ec5-bc29-1da1e67bbf4a
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
Front end the delivery of processes to data extraction, transformation, and load from disparate sources into a form that is consumable by analytics processes, for projects with moderate complexity, us…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
SAP BW / Analytics Cloud Consultant
domain · SAP CASE DOMAINslug: sap-bw-analytics-cloud-consultant · id: 165 · source: db
Domain=SAP; The JD is centered on data extraction, transformation, loading, warehousing, dimensional modeling, and analytics-oriented batch/data pipeline work, which best matches SAP BW / Analytics Cloud responsibilities.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
HIR ING Job Skills SAP Busn Obj Data Servi/Integr, Data Management, BI/BA Description 1" aria-hidden="false" style="color: rgba(0, 0, 0, 0.85); font-size: 12px;">Job Description 1" aria-hidden="false">Job Description SAP BODs Sr. Developer Lead Responsibilities Front end the delivery of processes to data extraction, transformation, and load from disparate sources into a form that is consumable by analytics processes, for projects with moderate complexity, using strong technical capabilities and sense of database performance Sound understanding of dimensional data modelling standards and best practices to ensure high quality Batch Processing - Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period Data Integration (Sourcing, Storage and Migration) - Capability to designand implement models, capabilities, and solutions to manage data within the enterprise (structured and unstructured, data archiving principles, data warehousing, data sourcing, etc.). This includes the data models, storage requirements and migration of data from one system to another Data Quality, Profiling and Cleansing - Capability to review (profile) a data set to establish its quality against a defined set of parameters and to highlight data where corrective action (cleansing) is required to remediat the data Stream Systems - Capability to discover, integrate, and ingest all available data from the machines that produce it, as fast as it is produced, in any format, and at any quality Excellent interpersonal skills to build network with variety of department across business to understand data and deliver business value and may interface and communicate with program teams, management and stakeholders as required to deliver small to medium-sized projects Essential Skills 3+ years’ experience in developing large scale data pipelines in a cloud/On-prem environment. Highly Proficient in any or more of market leading ETL tools like Informatica, DataStage, SSIS, Talend, etc., Fundamental knowledge in Data warehouse/Data Mart architecture and modelling Define and develop data ingest, validation, and transform pipelines. Fundamental knowledge of distributed data processing and storage Fundamental knowledge of working with structured, unstructured, and For cloud data engineer, experience with ETL/ELT patterns, preferably
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- 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
- 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
|
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
- 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
- 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
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Informatica (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Integration Platform
- Vendor
- Informatica
- License
- proprietary
- Year introduced
- 1993
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Informatica appears frequently in enterprise data-integration and ETL job postings, especially alongside cloud migration and MDM roles; it remains a common hiring keyword rather than a sunset technology.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 114
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- 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
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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Cloud (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Domain
- Sub-category
- Cloud Computing
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Cloud is a hiring-pipeline staple: AWS, Azure, and GCP appear in a large share of modern infrastructure JDs, and major vendors continue expanding cloud services rather than sunsetting them.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 37
- Sub-category id
- 1177
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | 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
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- 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 |
|---|---|---|---|---|---|---|
| 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 |
| Informatica | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Cloud | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | SAP BODS | 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 | |
| canonical_skill_proposed | Data Integration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Batch Processing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Profiling | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Cleansing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | DataStage | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SSIS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Talend | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL/ELT | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Distributed Data Processing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Distributed Data Storage | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | On-prem | type=Infrastructure 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
Show raw JSON
{
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},
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"source_marker": {
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},
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"word_count": 263
},
{
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},
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"word_count": 75
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
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},
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"stage3_signals": {
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"similarity": 0.6092
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{
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"sentence": "Data Integration (Sourcing, Storage and Migration) - Capability to design and implement models, capabilities, and solutions to manage data within the enterprise (structured and unstructured, data archiving principles, data warehousing, data sourcing, etc. ).",
"similarity": 0.6016
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Highly Proficient in any or more of market leading ETL tools like Informatica, DataStage, SSIS, Talend, etc. , Fundamental knowledge in Data warehouse/Data Mart architecture and modelling Define and develop data ingest, validation, and transform pipelines.",
"similarity": 0.5955
}
],
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"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Data Integration (Sourcing, Storage and Migration) - Capability to design and implement models, capabilities, and solutions to manage data within the enterprise (structured and unstructured, data archiving principles, data warehousing, data sourcing, etc. ).",
"similarity": 0.5433
},
{
"kra_text": "backend data integration",
"sentence": "Front end the delivery of processes to data extraction, transformation, and load from disparate sources into a form that is consumable by analytics processes, for projects with moderate complexity, using strong technical capabilities and sense of database performance",
"similarity": 0.5141
},
{
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"sentence": "This includes the data models, storage requirements and migration of data from one system to another",
"similarity": 0.4619
}
],
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"score": 0.5064,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "persistence and data modeling",
"sentence": "Sound understanding of dimensional data modelling standards and best practices to ensure high quality Batch Processing - Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period",
"similarity": 0.4869
},
{
"kra_text": "persistence and data modeling",
"sentence": "Data Integration (Sourcing, Storage and Migration) - Capability to design and implement models, capabilities, and solutions to manage data within the enterprise (structured and unstructured, data archiving principles, data warehousing, data sourcing, etc. ).",
"similarity": 0.4851
},
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}
],
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"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
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"matched_skills": null,
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],
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"alias_collision_detected": false,
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}
}
API 2 — extract-details
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{
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{
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{
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{
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}
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}
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},
{
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{
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{
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},
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},
{
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},
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},
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},
{
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},
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},
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},
{
"aliases_in_db": [
{
"alias_text": "Informatica",
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"id": 311,
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"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
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"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "informatica",
"sub_category_id": 114,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
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"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Informatica",
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{
"display_name": "Data Engineer",
"id": 2,
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
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},
{
"aliases_in_db": [],
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},
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},
{
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},
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},
{
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},
{
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},
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},
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},
{
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{
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},
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},
{
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{
"alias_text": "Cloud",
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"id": 2518,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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"source": "db"
},
"input_skill": "Cloud",
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},
{
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"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
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"slug": "cybersecurity-engineer",
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},
{
"display_name": "Data Engineer",
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},
{
"display_name": "DevOps Engineer",
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},
{
"display_name": "Fullstack Developer",
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},
{
"display_name": "Go Backend Developer",
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},
{
"display_name": "Java Backend Developer",
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"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": "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"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-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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},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
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}
]
}
],
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"new_alias_persisted": false,
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "On-prem",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Infrastructure Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
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},
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"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"SAP BODS",
"ETL",
"Data Integration",
"Data Warehousing",
"Batch Processing",
"Data Quality",
"Data Profiling",
"Data Cleansing",
"Data Pipelines",
"DataStage",
"SSIS",
"Talend",
"ETL/ELT",
"Distributed Data Processing",
"Distributed Data Storage",
"On-prem"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "SAP BW / Analytics Cloud Consultant",
"id": 165,
"rationale": "Domain=SAP; The JD is centered on data extraction, transformation, loading, warehousing, dimensional modeling, and analytics-oriented batch/data pipeline work, which best matches SAP BW / Analytics Cloud responsibilities.",
"role_archetype": null,
"slug": "sap-bw-analytics-cloud-consultant",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "SAP BODS",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Data Integration",
"tag": "new"
},
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "Data Modeling",
"tag": "in_db"
},
{
"skill": "Batch Processing",
"tag": "new"
},
{
"skill": "Data Quality",
"tag": "new"
},
{
"skill": "Data Profiling",
"tag": "new"
},
{
"skill": "Data Cleansing",
"tag": "new"
},
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Informatica",
"tag": "in_db"
},
{
"skill": "DataStage",
"tag": "new"
},
{
"skill": "SSIS",
"tag": "new"
},
{
"skill": "Talend",
"tag": "new"
},
{
"skill": "ETL/ELT",
"tag": "new"
},
{
"skill": "Distributed Data Processing",
"tag": "new"
},
{
"skill": "Distributed Data Storage",
"tag": "new"
},
{
"skill": "Cloud",
"tag": "in_db"
},
{
"skill": "On-prem",
"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": 165,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
"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",
"source": "db"
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