Pipeline run
5c740f5f-a54f-4fe5-944b-7eadd2c9ad6c
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. Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions 2. Model and design the application data structure storage and integration 3. IBM I…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Backend Developer
CASE Aslug: backend-engineer · id: 1 · source: db
Exact alias hit on backend-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.14 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About Accenture: Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 514,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com Accenture | Let there be change We embrace change to create 360-degree value www.accenture.com Project Role :Application Developer Project Role Description :Design, build and configure applications to meet business process and application requirements. Management Level :8 Work Experience :8-10 years Work location :Bengaluru Must Have Skills :IBM Cloud Pak - Data Good To Have Skills :No Technology Specialization Job Requirements : Key Responsibilities : 1. Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions2. Model and design the application data structure storage and integration 3. IBM InfoSphere Suite of products Designated Capability Expert and provide SME support4. Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team 5. RFP proposal with ADM estimator and Solution Approach Technical Experience : 1. Experience to deploy Cloud Pak for Data for clients across various platforms 2. Experience Cloud Pak for Data services and able to do integration between different services 3. Strong experience on lDataStage integration with cloud Hadoop ecosystem real time stages Professional Attributes : 1. Good communication skills and interpersonal skills2. Should have prior experience of leading a team being cooperative,collaborative, empathetic with the team members3. Should have strong analytical abilities and creative problem solving skills4. Should have the ability to adapt to changes quickly Educational Qualification : B-tech or BE 15 years of full time education
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
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
React Frontend Development
d_init_01
|
— | — |
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
- STABLE
- Typical lifespan
- EVERGREEN
- 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
- 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
- Data Engineering Tools
- Sub-category
- Cloud Services
- Skill nature
- PLATFORM
- 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
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- 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) |
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 Lake | new |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Lake | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Warehouse | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | IBM InfoSphere | type=Data Engineering Tools subtype=ETL Tools nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Cloud Pak for Data | type=Data Engineering Tools subtype=Cloud Services nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | DataStage | type=Data Engineering Tools subtype=ETL Tools nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Lake ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Data Lake ↔ React Frontend Development |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Accenture is a global professional",
"last_5_words": "and shared success for our clients"
},
"text": "Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world\u0027s largest network of Advanced Technology and Intelligent Operations centers. Our 514,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.",
"word_count": 84
},
"certifications": [],
"company_name": "Accenture",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Any Discipline",
"raw": "B-tech or BE",
"requirement": "required"
}
],
"experience": {
"max": 10,
"min": 8,
"raw": "8-10 years"
},
"job_locations": [
{
"aliases": [
"Bangalore"
],
"city": "Bengaluru",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Application Developer",
"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Designing enterprise data architecture",
"last_5_words": "and Solution Approach"
},
"text": "1. Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions\n2. Model and design the application data structure storage and integration\n3. IBM InfoSphere Suite of products Designated Capability Expert and provide SME support\n4. Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team\n5. RFP proposal with ADM estimator and Solution Approach",
"word_count": 56
},
{
"bullet_count": 3,
"heading": "Technical Experience",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Experience to deploy Cloud",
"last_5_words": "real time stages"
},
"text": "1. Experience to deploy Cloud Pak for Data for clients across various platforms\n2. Experience Cloud Pak for Data services and able to do integration between different services\n3. Strong experience on lDataStage integration with cloud Hadoop ecosystem real time stages",
"word_count": 42
},
{
"bullet_count": 4,
"heading": "Professional Attributes",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Good communication skills and",
"last_5_words": "to changes quickly"
},
"text": "1. Good communication skills and interpersonal skills\n2. Should have prior experience of leading a team being cooperative, collaborative, empathetic with the team members\n3. Should have strong analytical abilities and creative problem solving skills\n4. Should have the ability to adapt to changes quickly",
"word_count": 52
}
],
"urls": [
{
"type": "website",
"url": "http://www.accenture.com"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Data Lake"
},
{
"is_primary": true,
"skill_name": "Data Warehouse"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "IBM InfoSphere"
},
{
"is_primary": true,
"skill_name": "Cloud Pak for Data"
},
{
"is_primary": true,
"skill_name": "DataStage"
},
{
"is_primary": true,
"skill_name": "Hadoop"
}
],
"jd_role": {
"display_name": "Application Developer",
"rationale": null,
"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Accenture is a global professional",
"last_5_words": "and shared success for our clients"
},
"text": "Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services-all powered by the world\u0027s largest network of Advanced Technology and Intelligent Operations centers. Our 514,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.",
"word_count": 84
},
"certifications": [],
"company_name": "Accenture",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Any Discipline",
"raw": "B-tech or BE",
"requirement": "required"
}
],
"experience": {
"max": 10,
"min": 8,
"raw": "8-10 years"
},
"job_locations": [
{
"aliases": [
"Bangalore"
],
"city": "Bengaluru",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Application Developer",
"role_aliases": [
"App Developer",
"Software Developer",
"Application Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 5,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Designing enterprise data architecture",
"last_5_words": "and Solution Approach"
},
"text": "1. Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions\n2. Model and design the application data structure storage and integration\n3. IBM InfoSphere Suite of products Designated Capability Expert and provide SME support\n4. Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team\n5. RFP proposal with ADM estimator and Solution Approach",
"word_count": 56
},
{
"bullet_count": 3,
"heading": "Technical Experience",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Experience to deploy Cloud",
"last_5_words": "real time stages"
},
"text": "1. Experience to deploy Cloud Pak for Data for clients across various platforms\n2. Experience Cloud Pak for Data services and able to do integration between different services\n3. Strong experience on lDataStage integration with cloud Hadoop ecosystem real time stages",
"word_count": 42
},
{
"bullet_count": 4,
"heading": "Professional Attributes",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Good communication skills and",
"last_5_words": "to changes quickly"
},
"text": "1. Good communication skills and interpersonal skills\n2. Should have prior experience of leading a team being cooperative, collaborative, empathetic with the team members\n3. Should have strong analytical abilities and creative problem solving skills\n4. Should have the ability to adapt to changes quickly",
"word_count": 52
}
],
"urls": [
{
"type": "website",
"url": "http://www.accenture.com"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "5c740f5f-a54f-4fe5-944b-7eadd2c9ad6c",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 1.0,
"slug": "backend-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"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": "Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions",
"similarity": 0.6247
},
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Strong experience on lDataStage integration with cloud Hadoop ecosystem real time stages",
"similarity": 0.5315
},
{
"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": "IBM InfoSphere Suite of products Designated Capability Expert and provide SME support",
"similarity": 0.5174
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5579,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
{
"kra_text": "application data modeling",
"sentence": "Model and design the application data structure storage and integration",
"similarity": 0.646
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team",
"similarity": 0.5723
},
{
"kra_text": "application data modeling",
"sentence": "Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions",
"similarity": 0.4484
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 87,
"score": 0.5556,
"slug": "scala-backend-developer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Model and design the application data structure storage and integration",
"similarity": 0.5529
},
{
"kra_text": "performance tuning",
"sentence": "Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team",
"similarity": 0.5412
},
{
"kra_text": "backend data integration",
"sentence": "Experience Cloud Pak for Data services and able to do integration between different services",
"similarity": 0.5381
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.5441,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Java Backend Developer",
"kra_matches": [
{
"kra_text": "backend performance tuning",
"sentence": "Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team",
"similarity": 0.5555
},
{
"kra_text": "persistence and data modeling",
"sentence": "Model and design the application data structure storage and integration",
"similarity": 0.5337
},
{
"kra_text": "persistence and data modeling",
"sentence": "Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions",
"similarity": 0.4755
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 79,
"score": 0.5215,
"slug": "java-backend-developer",
"total_count": null
},
{
"display_name": "PHP Backend Developer",
"kra_matches": [
{
"kra_text": "data access and persistence patterns",
"sentence": "Model and design the application data structure storage and integration",
"similarity": 0.573
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Enterprise application performance calibration technical guidance to project team and suggest improvements best practices and recommendations to project team",
"similarity": 0.5723
},
{
"kra_text": "external system integration",
"sentence": "Experience Cloud Pak for Data services and able to do integration between different services",
"similarity": 0.4108
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 86,
"score": 0.5187,
"slug": "php-backend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Hadoop"
],
"role_id": 2,
"score": 0.1429,
"slug": "data-engineer",
"total_count": 7
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 1.0,
"slug": "backend-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on backend-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.14 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 1692,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 1836,
"r_and_r_preview": "1. Designing enterprise data architecture and designing building solution for Data Lake Data Warehouse ETL solutions\n2. Model and design the application data structure storage and integration\n3. IBM I",
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 24084,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Data Lake",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 24086,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Data Warehouse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 24087,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 24088,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "IBM InfoSphere",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 24089,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "Cloud Pak for Data",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 24090,
"role_display_name": "Backend Developer",
"role_slug": "backend-engineer",
"skill_name": "DataStage",
"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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 2017,
"existing_alias_text": "Data Lakes",
"input_term": "Data Lake",
"matched_canonical": {
"category_id": 1,
"display_name": "Data Lakes",
"id": 1358,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PATTERN",
"slug": "data-lakes",
"sub_category_id": 1025,
"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": 2010,
"existing_alias_text": "Hadoop",
"input_term": "Hadoop",
"matched_canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "Backend Developer",
"id": 1,
"rationale": "Exact alias hit on backend-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.14 does not contradict",
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"input_skill": "Data Lake",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"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": "Data Lake",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_final_skills": [
"Data Lake",
"Data Warehouse",
"ETL",
"IBM InfoSphere",
"Cloud Pak for Data",
"DataStage",
"Hadoop"
],
"input_llm_skills": [
"Data Lake",
"Data Warehouse",
"ETL",
"IBM InfoSphere",
"Cloud Pak for Data",
"DataStage",
"Hadoop"
],
"new_aliases_persisted": 0,
"run_id": "5c740f5f-a54f-4fe5-944b-7eadd2c9ad6c",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Data Lakes",
"alias_type": "CANONICAL",
"id": 2017,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 1,
"display_name": "Data Lakes",
"id": 1358,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PATTERN",
"slug": "data-lakes",
"sub_category_id": 1025,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"input_skill": "Data Lake",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"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": "Data Lake",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Data Lake",
"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": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Warehouse",
"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": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-warehouse",
"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": [],
"canonical": null,
"dimensions": [],
"input_skill": "IBM InfoSphere",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "ETL Tools",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ibm-infosphere",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Cloud Pak for Data",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PLATFORM",
"sub_category": "Cloud Services",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "cloud-pak-for-data",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "DataStage",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "ETL Tools",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "datastage",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Hadoop",
"alias_type": "CANONICAL",
"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hadoop",
"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": [
"Data Warehouse",
"ETL",
"IBM InfoSphere",
"Cloud Pak for Data",
"DataStage"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Backend Developer",
"id": 1,
"rationale": "Exact alias hit on backend-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.14 does not contradict",
"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"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "Data Warehouse",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "IBM InfoSphere",
"tag": "new"
},
{
"skill": "Cloud Pak for Data",
"tag": "new"
},
{
"skill": "DataStage",
"tag": "new"
},
{
"skill": "Hadoop",
"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": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "Data Lake",
"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": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 1,
"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"
},
"dimension_id": 96,
"input_skill": "Data Lake",
"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": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Hadoop",
"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": 1351,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 2
},
"planner_output": null,
"run_id": "5c740f5f-a54f-4fe5-944b-7eadd2c9ad6c"
}