Pipeline run
5c1843ea-a44f-45c9-9852-f95fde35361a
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD centers on data pipelines, ETL/ELT, data modeling, reporting infrastructure, and technical leadership for analytics/automation, which best matches Data Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Title Lead Analyst I - IT Enterprise Reporting Summary This position reports to Momentive’s Information Technology function and is strongly aligned to support all business functions within the Momentive to drive digital transformation. This individual will be responsible for technical leadership for reporting team. Responsible for architecture, design, implementation, including introduction of new technologies and tools relating to Enterprise Reporting, Data management, Enterprise data warehouse. Driving continuous improvement and optimizing effectiveness of existing reporting systems including integration of different systems, identification of superior solutions and developing and expanding data science capabilities including machine learning and AI. Responsibilities Include • Provide Technical leadership for reporting team • Understand the current state reporting and automation solution, identify & work on improvement areas • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data reporting needs. • Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics. • Assemble large, complex data sets that meet functional / non-functional business requirements. • Utilizing knowledge of ETL and Data modeling drive and execute systems with a variety of large structured and unstructured sources. Work with data and drive analytics. • Drive all aspects of the Digital transformation, Data management, Reporting and Data Science capabilities through PoC’s, Projects to introduce new capabilities. • Engage with external vendors / internal teams on all aspects of project execution. • Build the infrastructure required for ELT from a wide variety of data sources. • Responsible for system administration, design, architecture, and continuous improvement across platforms Decision Making Authority • Drive/lead projects relating to digital transformation. • Lead decisions relating to technology fit for business case • Drive/lead new technology vendor assessment Key Metrics Role Is Accountable For • Development of 5 year Reporting strategy • Driving and managing Automation and reporting vertical • Leading the reporting team • Introduction of new tools and technologies relating to Data Science, AI and Machine learning Qualifications The following are required for the role • Bachelor’s degree in Computer Science, Business, Math, Statistics, Engineering or related field or equivalent is required. • Strong experience in working directly with business users to identify, define, analyze, test and implement reporting needs and platforms • Experience with and in developing data visualizations through tools such as Tableau, SAC, etc. to deliver thought provoking analytical information to the business • Experience with Relational and NoSQL Database knowledge like MSSQL, MySQL, SAP ECC, SAP BW, Snowflake, Azure Data factory, Azure Data lake, Azure Data bricks and SQL Analysis Service or similar environments • Sound knowledge of ETL, Data modeling, Statistical and Data Science concepts • Experience in handling structured and unstructured data The Following Are Preferred For The Role • Master’s or other advanced degree in Computer Science, Business, Math, Statistics, Engineering or related field preferred. • Manufacturing or business experience with a solid understanding of business operations /processes • Working knowledge of systems like Snowflake, Azure data lake, SAP, SAP BW, Salesforce, etc. • Exposure to cloud technologies: Azure (Preferred)/AWS/GCP, etc • Experience in tools ETL like Alteryx/Informatica or similar environments • Knowledge of SAP ECC 6.0 Modules (SD, MM, PP , FI/CO, QM, PM) • Experience with Finance Consolidation tools (BFC) What We Offer At Momentive, we value your well-being and offer competitive total rewards and development programs. Our inclusive culture fosters a strong sense of belonging and provides diverse career opportunities to help you unleash your full potential. Together, through innovative problem-solving and collaboration, we strive to create sustainable solutions that make a meaningful impact. Join our Momentive team to open a bright future. #BePartoftheSolution About Us Momentive is a premier global advanced materials company with a cutting-edge focus on silicones and specialty products. We deliver solutions designed to help propel our customer’s products forward—products that have a profound impact on all aspects of life, around the clock and from living rooms to outer space. With every innovation, Momentive creates a more sustainable future. Our vast product portfolio is made up of advanced silicones and specialty solutions that play an essential role in driving performance across a multitude of industries, including agriculture, automotive, aerospace, electronics, energy, healthcare, personal care, consumer products, building and construction, and more. Momentive believes a diverse workforce empowers our people, strengthens our business, and contributes to a sustainable world. We are proud to be an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any characteristic protected by law. To be considered for this position candidates are required to submit an application for employment and be of legal working age as defined by local law. An offer may be conditioned upon the successful completion of pre-employment conditions, as applicable, and subject to applicable laws and regulations. Note to third parties: Momentive is not seeking or accepting any unsolicited assistance from search and selection firms or employment agencies at this time.
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
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
- 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
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AI (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Artificial Intelligence
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles; major vendors like Microsoft, Google, and AWS have broad AI offerings and hiring demand reflects mainstream adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1020
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Machine Learning
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1024
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
AI Governance and Model Security Catalog dimension db id 50
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | 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 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 |
| AI | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
React Frontend Development
d_init_01
|
✓ | — | 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 | ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Science | type=Concepts 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
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Momentive is a premier global",
"last_5_words": "building and construction, and more."
},
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"certifications": [],
"company_name": "Momentive",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Business Operations",
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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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"Enterprise Reporting Analyst"
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"roles_and_responsibilities": [
{
"bullet_count": 10,
"heading": "Responsibilities Include",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Provide Technical leadership for",
"last_5_words": "design, architecture, and continuous improvement"
},
"text": "\u2022 Provide Technical leadership for reporting team\n\u2022 Understand the current state reporting and automation solution, identify \u0026 work on improvement areas\n\u2022 Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data reporting needs.\n\u2022 Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.\n\u2022 Assemble large, complex data sets that meet functional / non-functional business requirements.\n\u2022 Utilizing knowledge of ETL and Data modeling drive and execute systems with a variety of large structured and unstructured sources. Work with data and drive analytics.\n\u2022 Drive all aspects of the Digital transformation, Data management, Reporting and Data Science capabilities through PoC\u2019s, Projects to introduce new capabilities.\n\u2022 Engage with external vendors / internal teams on all aspects of project execution.\n\u2022 Build the infrastructure required for ELT from a wide variety of data sources.\n\u2022 Responsible for system administration, design, architecture, and continuous improvement across platforms",
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{
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"word_count": 24
},
{
"bullet_count": 4,
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"heading_was_present": true,
"source_marker": {
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},
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"word_count": 36
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"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Data Modeling"
},
{
"is_primary": true,
"skill_name": "ELT"
},
{
"is_primary": true,
"skill_name": "Data Science"
},
{
"is_primary": true,
"skill_name": "AI"
},
{
"is_primary": true,
"skill_name": "Machine Learning"
}
],
"jd_role": {
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"Lead Analyst",
"IT Analyst",
"Enterprise Reporting Analyst"
],
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"slug": ""
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"nano_parsed": {
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"word_count": 84
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"kra_match_roles": [
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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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"sentence": "Provide Technical leadership for reporting team",
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{
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},
{
"display_name": "Flutter Developer",
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{
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},
{
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},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Engage with external vendors / internal teams on all aspects of project execution.",
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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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"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 13,
"score": 0.1667,
"slug": "ai-engineer",
"total_count": 6
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 16,
"score": 0.1667,
"slug": "ml-ops-engineer",
"total_count": 6
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.95,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.95,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Reporting Platform Engineering",
"Data Pipeline / ELT Engineering",
"Data Modeling",
"Analytics Enablement",
"Digital Transformation Leadership",
"Vendor and Technology Evaluation",
"Reporting Strategy and Automation"
],
"matched_kras": [
"Provide Technical leadership for reporting team",
"Identify \u0026 work on improvement areas",
"Build analytics tools that utilize the data pipeline",
"Assemble large, complex data sets",
"Drive and execute systems with large structured and unstructured sources",
"Build the infrastructure required for ELT",
"Responsible for system administration, design, architecture, and continuous improvement",
"Development of 5 year Reporting strategy",
"Drive/lead projects relating to digital transformation",
"Lead decisions relating to technology fit for business case"
],
"matched_skills": [
"ETL",
"Data modeling",
"ELT",
"data pipeline",
"data sources",
"system administration",
"architecture",
"automation",
"Data Science",
"AI",
"machine learning"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD centers on data pipelines, ETL/ELT, data modeling, reporting infrastructure, and technical leadership for analytics/automation, which best matches Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 334,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 15538,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 15539,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Modeling",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 15540,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ELT",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 15541,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Science",
"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": 5644,
"existing_alias_text": "Domain Modeling",
"input_term": "Data Modeling",
"matched_canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1990,
"existing_alias_text": "AI",
"input_term": "AI",
"matched_canonical": {
"category_id": 2,
"display_name": "AI",
"id": 1347,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "ai",
"sub_category_id": 1020,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2015,
"existing_alias_text": "Machine Learning",
"input_term": "Machine Learning",
"matched_canonical": {
"category_id": 2,
"display_name": "Machine Learning",
"id": 1356,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "machine-learning",
"sub_category_id": 1024,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
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"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
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},
{
"display_name": "Node.js Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
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},
{
"display_name": "PHP Backend Developer",
"id": 86,
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"role_archetype": "Engineering",
"slug": "php-backend-developer",
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},
{
"display_name": "Ruby Backend Developer",
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"role_archetype": "Engineering",
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},
{
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"id": 87,
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"role_archetype": "Engineering",
"slug": "scala-backend-developer",
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},
{
"display_name": "AI Engineer",
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"role_archetype": null,
"slug": "ai-engineer",
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},
{
"display_name": "ML Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
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}
],
"chosen_role": {
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"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on data pipelines, ETL/ELT, data modeling, reporting infrastructure, and technical leadership for analytics/automation, which best matches Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"id": 293,
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"slug": "application-architecture-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
"id": 18,
"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
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"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": "AI",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "AI Governance and Model Security",
"id": 50,
"rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
"slug": "ai-governance-and-model-security",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"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"
}
]
},
{
"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": "Machine Learning",
"llm_role": null,
"roles_from_db": []
}
],
"input_final_skills": [
"ETL",
"Data Modeling",
"ELT",
"Data Science",
"AI",
"Machine Learning"
],
"input_llm_skills": [
"ETL",
"Data Modeling",
"ELT",
"Data Science",
"AI",
"Machine Learning"
],
"new_aliases_persisted": 0,
"run_id": "5c1843ea-a44f-45c9-9852-f95fde35361a",
"skills_detail": [
{
"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": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
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"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
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"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "etl",
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"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "domain modeling",
"alias_type": "CANONICAL",
"id": 3675,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Domain Modeling",
"alias_type": "CANONICAL",
"id": 5644,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "domain modeling",
"id": 2379,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "domain-modeling",
"sub_category_id": 2831,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Application Architecture Patterns",
"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"
},
"input_skill": "Data Modeling",
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Design Patterns",
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"rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
"slug": "service-architecture-and-design-patterns",
"source": "db"
},
"input_skill": "Data Modeling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Data Modeling",
"matched_via": "embedding_alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ELT",
"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,
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"skill_id": "elt",
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},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Science",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concepts",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
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"merge_log": [],
"placed": null,
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"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "AI",
"alias_type": "CANONICAL",
"id": 1990,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "AI",
"id": 1347,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "ai",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "AI",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "AI",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Machine Learning",
"alias_type": "CANONICAL",
"id": 2015,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Machine Learning",
"id": 1356,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "machine-learning",
"sub_category_id": 1024,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "AI Governance and Model Security",
"id": 50,
"rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
"slug": "ai-governance-and-model-security",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"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"
}
]
},
{
"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": "Machine Learning",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Machine Learning",
"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": [
"ETL",
"ELT",
"Data Science"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD centers on data pipelines, ETL/ELT, data modeling, reporting infrastructure, and technical leadership for analytics/automation, which best matches Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "ETL",
"tag": "new"
},
{
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LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.