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Pipeline run

5c1843ea-a44f-45c9-9852-f95fde35361a

Pipeline LLM cost (USD)
API 1: $0.0079 API 2: $0.0002 API 3: $0.0000 Total: $0.0081

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Lead the reporting/automation team to design and improve ETL/ELT, data modeling and analytics tooling that turns large multi-source data into business metrics and insights, while driving digital transformation, vendor evaluation and reporting strategy.
"“Build the infrastructure required for ELT from a wide variety of data sources.”"
Tech stack maturity
AI-Native & Bleeding-Edge
The role centers on AI and machine learning, which most strongly aligns with an AI-native, cutting-edge technology stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI, Machine Learning
Evidence — skills matched in JD (6)
ETL Data Modeling ELT Data Science AI Machine Learning
Skill cluster (2 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Cross-cutting / unaligned
ETL Data Modeling ELT Data Science AI
Show KRA description ↓
• 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 • Drive/lead projects relating to digital transformation. • Lead decisions relating to technology fit for business case • Drive/lead new technology vendor assessment • 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

Signals

Skill ml-engineer
0.17
Alias
KRA data-engineer
0.62

Post-classification

Centroidupdated · n=334
Alias collision log
New-role queue
New skills captured4
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
Data Modeling primary Data Engineer pending
ELT primary Data Engineer pending
Data Science primary Data Engineer pending
Status: completed Created: 2026-05-27T15:39:01.611793Z Updated: 2026-06-12T16:08:47.786148Z API 3 duration: 7110 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: 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

ETLData modelingELTdata pipelinedata sourcessystem administrationarchitectureautomationData ScienceAImachine learning

Matched dimensions

Reporting Platform EngineeringData Pipeline / ELT EngineeringData ModelingAnalytics EnablementDigital Transformation LeadershipVendor and Technology EvaluationReporting Strategy and Automation

Matched KRAs

Provide Technical leadership for reporting teamIdentify & work on improvement areasBuild analytics tools that utilize the data pipelineAssemble large, complex data setsDrive and execute systems with large structured and unstructured sourcesBuild the infrastructure required for ELTResponsible for system administration, design, architecture, and continuous improvementDevelopment of 5 year Reporting strategyDrive/lead projects relating to digital transformationLead decisions relating to technology fit for business case

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

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.

ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain modeling id=2379 · domain-modeling

Aliases — catalog

  • domain modeling (CANONICAL) primary
  • Domain Modeling (CANONICAL)

Context tags (catalog)

CQRS DDD ERD UML aggregate bounded context business logic context map context mapping data modeling domain events domain-driven design entities entity event sourcing event storming microservices repositories repository pattern service layer services value object value objects

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
ELT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Science Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
AI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AI id=1347 · ai

Aliases — catalog

  • AI (CANONICAL)

Context tags (catalog)

AI ethics PyTorch TensorFlow algorithm optimization computer vision data preprocessing deep learning feature engineering machine learning model training natural language processing neural networks predictive analytics reinforcement learning supervised learning

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)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

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
RoleLead Analyst I - IT Enterprise Reporting
CompanyMomentive
DomainManufacturing
JD type pass
Show raw JSON
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      "requirement": "required"
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  "urls": []
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API 1 — extract-from-jd click to toggle
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  "final_skills": [
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      "is_primary": true,
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  "stage3_signals": {
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            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
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            "sentence": "Assemble large, complex data sets that meet functional / non-functional business requirements.",
            "similarity": 0.6028
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6188,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
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            "similarity": 0.5697
          },
          {
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            "similarity": 0.5199
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.5354,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": [
          {
            "kra_text": "facilitate technical and delivery decisions",
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            "similarity": 0.5945
          },
          {
            "kra_text": "Set team goals and delivery plans",
            "sentence": "Provide Technical leadership for reporting team",
            "similarity": 0.5007
          },
          {
            "kra_text": "Set team goals and delivery plans",
            "sentence": "Engage with external vendors / internal teams on all aspects of project execution.",
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 121,
        "score": 0.5188,
        "slug": "engineering-manager",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "collaborate with design, product, and backend teams",
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          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Responsible for system administration, design, architecture, and continuous improvement across platforms",
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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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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.4988,
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      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Maintains ML platform runbooks, on-call escalation playbooks, and deployment procedure documentation for production operations teams.",
            "sentence": "Responsible for system administration, design, architecture, and continuous improvement across platforms",
            "similarity": 0.5085
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
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            "similarity": 0.4995
          },
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
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        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4914,
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    ],
    "skill_match_roles": [
      {
        "display_name": "ML Engineer",
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        "matched_count": 1,
        "matched_skills": [
          "Machine Learning"
        ],
        "role_id": 3,
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        "total_count": 6
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": null,
        "matched_count": 1,
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          "Machine Learning"
        ],
        "role_id": 13,
        "score": 0.1667,
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        "total_count": 6
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
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          "Machine Learning"
        ],
        "role_id": 16,
        "score": 0.1667,
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        "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
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      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
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      "input_term": "Data Modeling",
      "matched_canonical": {
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        "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,
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        "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": [
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      "display_name": ".NET Backend Developer",
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    },
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      "slug": "ml-engineer",
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      ]
    },
    {
      "dimension": {
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      },
      "input_skill": "AI",
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      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "source": "db"
      },
      "input_skill": "Machine Learning",
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    },
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    }
  ],
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  "new_aliases_persisted": 0,
  "run_id": "5c1843ea-a44f-45c9-9852-f95fde35361a",
  "skills_detail": [
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      "new_skill_meta": {
        "derived": {
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      },
      "source_tag": "llm",
      "was_in_llm_skills": true
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    {
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        {
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              "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,
        "relationships": null,
        "skill_id": "elt",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "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": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-science",
        "split_log": [],
        "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",
        "sub_category_id": 1020,
        "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"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Data Science",
      "tag": "new"
    },
    {
      "skill": "AI",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "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"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "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": ".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"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "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": "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"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "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": "AI",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1347,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 50,
        "input_skill": "Machine Learning",
        "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": "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1356,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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": "Machine Learning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1356,
        "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": "5c1843ea-a44f-45c9-9852-f95fde35361a"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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