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Pipeline run

57b138a1-6be1-41fe-b45d-222bbcf3a2d0

Pipeline LLM cost (USD)
API 1: $0.0099 API 2: $0.0002 API 3: $0.0000 Total: $0.0101

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 transformation and modeling
Design and tune database architectures, build ETL/data integration flows, and create models/schema for scalable, secure, compliant data use. Monitor performance, fix data quality/issues, and work with analysts, data scientists, and IT on best-practice governance.
"Create data models and schema designs for data efficiency."
Tech stack maturity
Mainstream Modern
Schema design is a core, widely used data engineering skill typically associated with established modern data platforms and practices rather than bleeding-edge or legacy-only stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 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):
Evidence — skills matched in JD (7)
ETL Database Performance Tuning Data Modeling Schema Design Data Integration Data Recovery Backup
Skill cluster (2 dimension groups, role-scoped)
Relational Data Modeling
Schema Design
Cross-cutting / unaligned
ETL Database Performance Tuning Data Modeling Data Integration Data Recovery Backup
Show KRA description ↓
The Database Architect with Data Engineering is a crucial position within the organization, responsible for designing, implementing, and managing complex database systems that meet the business's data needs. This role combines expertise in database architecture with a strong understanding of data engineering practices, ensuring seamless integration and optimized performance. The Database Architect will collaborate with data analysts, data scientists, and IT teams to develop scalable data solutions and data models that support various applications and analytical initiatives. This professional will also play a pivotal role in setting best practices regarding data management, security, and compliance, directly impacting the organization's ability to derive valuable insights from data. Moreover, this role requires ongoing monitoring and assessment of data strategies, adjusting as necessary to keep pace with evolving technologies and business requirements. Through innovative design and careful stewardship of data assets, the Database Architect will support the organization's goal of leveraging data as a strategic asset for operational and competitive advantage. • Design complex database architectures to support various applications. • Develop and implement ETL processes for effective data collection. • Ensure data security and compliance with relevant regulations. • Collaborate with data engineering teams to improve data access. • Optimize database performance and scalability through tuning. • Create data models and schema designs for data efficiency. • Evaluate and implement new database technologies and upgrades. • Monitor database systems for performance and reliability. • Conduct data integration from multiple sources into coherent datasets. • Provide best practices for data management and governance. • Assist in developing data recovery and backup strategies. • Work closely with stakeholders to ensure database solutions align with business objectives. • Analyze and resolve database performance and data quality issues. • Prepare technical documentation and guidelines for database usage. • Lead collaboration efforts across multidisciplinary teams for data projects.

Signals

Skill full-stack-engineer
0.20
Alias data-engineer
1.00
KRA data-engineer
0.66

Post-classification

Centroidupdated · n=66
Alias collision log#199
New-role queue
New skills captured6
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
Database Performance Tuning primary Data Engineer pending
Data Modeling primary Data Engineer pending
Data Integration primary Data Engineer pending
Data Recovery Data Engineer pending
Backup Data Engineer pending
Status: completed Created: 2026-05-27T13:42:39.682294Z Updated: 2026-05-27T13:43:31.246220Z API 3 duration: 8594 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

CASE D

slug: data-engineer · id: 2 · source: db

LLM2 picked data-engineer (confidence 0.80)

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

Overview

The Database Architect with Data Engineering is a crucial position within the organization, responsible for designing, implementing, and managing complex database systems that meet the business's data needs. This role combines expertise in database architecture with a strong understanding of data engineering practices, ensuring seamless integration and optimized performance. The Database Architect will collaborate with data analysts, data scientists, and IT teams to develop scalable data solutions and data models that support various applications and analytical initiatives. This professional will also play a pivotal role in setting best practices regarding data management, security, and compliance, directly impacting the organization's ability to derive valuable insights from data. Moreover, this role requires ongoing monitoring and assessment of data strategies, adjusting as necessary to keep pace with evolving technologies and business requirements. Through innovative design and careful stewardship of data assets, the Database Architect will support the organization's goal of leveraging data as a strategic asset for operational and competitive advantage.

Key Responsibilities

• Design complex database architectures to support various applications.
• Develop and implement ETL processes for effective data collection.
• Ensure data security and compliance with relevant regulations.
• Collaborate with data engineering teams to improve data access.
• Optimize database performance and scalability through tuning.
• Create data models and schema designs for data efficiency.
• Evaluate and implement new database technologies and upgrades.
• Monitor database systems for performance and reliability.
• Conduct data integration from multiple sources into coherent datasets.
• Provide best practices for data management and governance.
• Assist in developing data recovery and backup strategies.
• Work closely with stakeholders to ensure database solutions align with business objectives.
• Analyze and resolve database performance and data quality issues.
• Prepare technical documentation and guidelines for database usage.
• Lead collaboration efforts across multidisciplinary teams for data projects.


Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• Minimum of 5 years of experience in database architecture or data engineering.
• Proficiency in SQL and database query optimization.
• Strong experience with ETL tools and data integration techniques.
• Knowledge of cloud computing platforms such as AWS or Azure.
• Familiarity with big data technologies (e.g., Hadoop, Spark).
• Experience in data security and compliance best practices.
• Strong analytical skills and problem-solving abilities.
• Understanding of data warehousing concepts and practices.
• Excellent communication and teamwork skills.
• Capability to stay updated with the latest technologies in data management.
• Certification in relevant database technologies (e.g., Oracle, Microsoft SQL Server) is preferred.
• Experience with scripting languages (e.g., Python, R) for automation.
• Detail-oriented with a commitment to data accuracy.
• Ability to lead projects and mentor junior staff when necessary.
• Willingness to adapt to evolving business demands and technology changes.


Skills: cloud computing (aws, azure),data warehousing,data security and compliance,performance tuning,database architecture,data integration,analytical skills,scripting languages (python, r),sql proficiency,data security,project management,technical documentation,etl processes,data engineering,big data technologies,big data technologies (hadoop, spark),sql,database management,data integration techniques,data modeling

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
Database Performance Tuning 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
Databases
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
Schema Design Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: schema design id=19 · schema-design

Aliases — catalog

  • schema design (CANONICAL) primary
  • Schema Design (CANONICAL)

Context tags (catalog)

ACID DDL ER modeling ERD NoSQL SQL cardinality constraints data governance data integrity data lineage data model data modeling data normalization data types data warehouse database design database normalization database optimization database schema denormalization dimensional modeling entity relationship entity-relationship entity-relationship diagram foreign key foreign keys indexes indexing metadata management normalization primary key primary keys query optimization relational relational databases relational model relational schema schema evolution schema migration snowflake schema star schema

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Schema Design
Confidence
0.84
Version strategy
NOT_APPLICABLE

Maturity reasoning: Schema design is a standard requirement in backend/data JDs and appears across SQL, data engineering, and SaaS roles; it’s a core interview topic rather than a niche specialty.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2479
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, PHP Backend Developer

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Integration 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 Recovery Secondary 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
Databases
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Backup Secondary 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
Databases
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
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
Schema Design in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Schema Design in_db
Relational Database Design
relational-database-design
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 Database Performance Tuning | type=Databases subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Integration | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Recovery | type=Databases subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Backup | type=Databases subtype=general nature=PRACTICE 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
RoleDatabase Architect with Data Engineering
ExperienceMinimum of 5 years of experience in database architecture or data engineering.
DomainIT Services & Consulting
JD type pass

Certifications

Certification in relevant database technologies (e.g., Oracle, Microsoft SQL Server)
Show raw JSON
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API 1 — extract-from-jd click to toggle
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  "final_skills": [
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  "stage3_signals": {
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    "kra_match_roles": [
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            "kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
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          },
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        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "schema design"
        ],
        "role_id": 1,
        "score": 0.2,
        "slug": "backend-engineer",
        "total_count": 5
      },
      {
        "display_name": "Python Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "schema design"
        ],
        "role_id": 80,
        "score": 0.2,
        "slug": "python-backend-developer",
        "total_count": 5
      },
      {
        "display_name": "Node.js Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "schema design"
        ],
        "role_id": 82,
        "score": 0.2,
        "slug": "node-backend-developer",
        "total_count": 5
      },
      {
        "display_name": ".NET Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "schema design"
        ],
        "role_id": 83,
        "score": 0.2,
        "slug": "dotnet-backend-developer",
        "total_count": 5
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": true,
    "case": "D",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 1.0,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.8,
    "is_new_role": false,
    "llm2_fired": true,
    "llm2_reasoning": "The JD\u2019s emphasis on database architecture, ETL processes, data modeling, performance tuning, security, and collaboration aligns closely with a Data Engineer\u2019s core responsibilities.",
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "LLM2 picked data-engineer (confidence 0.80)",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 66,
    "centroid_updated": true,
    "collision_log_id": 199,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 4311,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 4312,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Database Performance Tuning",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 4313,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 4314,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Integration",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 4315,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Recovery",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 4316,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Backup",
        "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": 147,
      "existing_alias_text": "schema design",
      "input_term": "Schema Design",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "schema design",
        "id": 19,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "schema-design",
        "sub_category_id": 2479,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
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      "role_archetype": "Engineering",
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    },
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      "slug": "kotlin-server-backend-developer",
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      "rationale": null,
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      "slug": "full-stack-engineer",
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  ],
  "chosen_role": {
    "display_name": "Data Engineer",
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    "rationale": "LLM2 picked data-engineer (confidence 0.80)",
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      "input_skill": "Data Modeling",
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    },
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  ],
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  "run_id": "57b138a1-6be1-41fe-b45d-222bbcf3a2d0",
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      "new_skill_meta": {
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    {
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      },
      "source_tag": "llm",
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    {
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        },
        {
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      ],
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            {
              "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": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Schema Design",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Integration",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-integration",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Recovery",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-recovery",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Backup",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "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": "backup",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Database Performance Tuning",
    "Data Integration",
    "Data Recovery",
    "Backup"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "LLM2 picked data-engineer (confidence 0.80)",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Database Performance Tuning",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Schema Design",
      "tag": "in_db"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "Data Recovery",
      "tag": "new"
    },
    {
      "skill": "Backup",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 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": "Relational Data Modeling",
          "id": 216,
          "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
          "slug": "relational-data-modeling",
          "source": "db"
        },
        "dimension_id": 216,
        "input_skill": "Schema Design",
        "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": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 19,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Database Design",
          "id": 4,
          "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
          "slug": "relational-database-design",
          "source": "db"
        },
        "dimension_id": 4,
        "input_skill": "Schema Design",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 19,
        "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": "57b138a1-6be1-41fe-b45d-222bbcf3a2d0"
}

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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