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Pipeline run

0ff2002c-5f95-4558-a9e8-432257524c42

Pipeline LLM cost (USD)
API 1: $0.0090 API 2: $0.0004 API 3: $0.0000 Total: $0.0094

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · ETL / Data Integration
Build and support ETL/data-integration pipelines in IBM DataStage, including data aggregation, cleansing, migration, and metadata/version management across environments. Also troubleshoot, document, and improve automation, coding standards, performance, and support processes.
"“Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems”"
Tech stack maturity
Mainstream Modern
ETL/ELT development with change-data-capture and data-mapping is a common modern data-integration stack, typically implemented with established tools and cloud or hybrid platforms rather than bleeding-edge AI-native patterns.
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 (14)
ETL IBM DataStage Data Warehousing Data Integration Database Replication Change Data Capture SOA ESB Normalized Data Model Dimensional Data Model Data Mapping Data Processing Project Management Automation
Skill cluster (1 dimension groups, role-scoped)
Cross-cutting / unaligned
ETL IBM DataStage Data Warehousing Data Integration Database Replication Change Data Capture SOA ESB Normalized Data Model Dimensional Data Model Data Mapping Data Processing Project Management Automation
Show KRA description ↓
• Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation. • Implement best practices and drive adoption around coding, design, quality, and performance. • Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise. • The overall experience of 7 to 10 years in the Information Technology. • Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices. • Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc.) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices. • Experience with both normalized and dimensional data models, hands-on knowledge of other data integration techniques such as database replication, change data capture (CDC) etc. and familiarity of SOA and ESB technologies and patterns. • Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables. • Complete work within a reasonable time frame under the supervision of a manager or team lead. • Plan and manage all aspects of the support function. • Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation. • Strong knowledge in project management practices and ability to document processes and procedures as needed. • Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision • Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables.

Signals

Skill data-engineer
0.08
Alias data-engineer
1.00
KRA data-engineer
0.54

Post-classification

Centroidupdated · n=12
Alias collision log
New-role queue
New skills captured12
New KRA capturedyes

Captured for admin review

ETL primary ETL / ELT Developer pending
IBM DataStage primary ETL / ELT Developer pending
Data Warehousing primary ETL / ELT Developer pending
Data Integration primary ETL / ELT Developer pending
Database Replication primary ETL / ELT Developer pending
SOA primary ETL / ELT Developer pending
ESB primary ETL / ELT Developer pending
Normalized Data Model primary ETL / ELT Developer pending
Dimensional Data Model primary ETL / ELT Developer pending
Data Processing primary ETL / ELT Developer pending
Project Management ETL / ELT Developer pending
Automation ETL / ELT Developer pending
R&R fragment (sim 0.00) ETL / ELT Developer pending

• Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation. • Implement best practices and drive adoption around coding, design, qualit…

Status: completed Created: 2026-05-27T15:56:46.087027Z Updated: 2026-06-12T15:42:51.715037Z API 3 duration: 5937 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

ETL / ELT Developer

domain · Data Engineering & Analytics CASE DOMAIN

slug: etl-elt-developer · id: 50 · source: db

Domain=Data Engineering & Analytics; The JD is centered on ETL development and DataStage-based data integration, metadata migration, and support of data processing systems, which best matches an ETL/ELT Developer.

Matched skills

ETLIBM DatastageData Warehousingdata mappingdatabase replicationchange data capture (CDC)SOAESBproject managementdata processing systems

Matched dimensions

ETL DevelopmentData IntegrationData WarehousingMetadata ManagementData MigrationProcess AutomationQuality and Performance ImprovementCross-functional Collaboration

Matched KRAs

contributing to the development and able to identify avenues for automationimplement best practices and drive adoption around coding, design, quality, and performancebuilding ETL interfaces using IBM Datastageaggregate, cleanse and migrate data across Data Warehousing systemscapture, version, and migrate IBM Datastage ETL metadataestablish standards, guidelines and best practicesanalyze information identify and formulate solutions to problemsplan and manage all aspects of the support functiondeveloping, testing and moving solutions to implementationdocument processes and procedures as needed

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
1
Skipped

Job description

Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto “ Building Success Together® ,” Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today’s world, with immersive and seamless experiences across digital and physical channels.

What You'll Do

• Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation.
• Implement best practices and drive adoption around coding, design, quality, and performance.
• Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise.


What You Know

• The overall experience of 7 to 10 years in the Information Technology.
• Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices.
• Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc.) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices.
• Experience with both normalized and dimensional data models, hands-on knowledge of other data integration techniques such as database replication, change data capture (CDC) etc. and familiarity of SOA and ESB technologies and patterns.


Demonstrable Skills:

• Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables.
• Complete work within a reasonable time frame under the supervision of a manager or team lead.
• Plan and manage all aspects of the support function.
• Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation.
• Strong knowledge in project management practices and ability to document processes and procedures as needed.
• Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision
• Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables


Education

• Bachelor’s / Master’s degree in specific technical fields like computer science, math, statistics preferred, or equivalent practical experience.


Benefits

• In addition to competitive salaries and benefits packages, Nisum India offers its employees some unique and fun extras:
• Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis. We support our team to excel in their field.
• Parental Medical Insurance - Nisum believes our team is the heart of our business and we want to make sure to take care of the heart of theirs. We offer opt-in parental medical insurance in addition to our medical benefits.
• Activities -From the Nisum Premier League's cricket tournaments to hosted Hack-a-thon, Nisum employees can participate in a variety of team building activities such as skits, dances performance in addition to festival celebrations.
• Free Meals - Free snacks and dinner is provided on a daily basis, in addition to subsidized lunch.


Nisum is an Equal Opportunity Employer and we are proud of our ongoing efforts to foster diversity and inclusion in the workplace.

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
IBM DataStage 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
ETL Tools
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehousing 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Database Replication 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Change Data Capture Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Change data capture id=140 · change-data-capture

Aliases — catalog

  • Change data capture (CANONICAL) primary

Context tags (catalog)

Debezium ELT ETL Kafka Connect WAL binlog data pipeline event sourcing incremental load logical replication replication slot snapshotting streaming ingestion transaction log upsert

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Data Capture Methodology
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: CDC is broadly adopted in data engineering; it appears in many JDs for Kafka/Debezium/ETL roles and is a standard pattern for near-real-time replication and sync.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
102
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Batch Ingestion and Replication Catalog dimension db id 29

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Batch Ingestion and Replication
batch-ingestion-and-replication
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SOA 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
Architecture Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ESB 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
Architecture Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Normalized Data Model 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Dimensional Data Model Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Dimensional modeling id=125 · dimensional-modeling

Aliases — catalog

  • Dimensional modeling (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP SCD Type 2 business intelligence conformed dimensions data warehouse dimension table drill-down fact table grain slowly changing dimension snowflake schema star schema surrogate key

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Data Modeling Concept
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in analytics/data-warehouse JDs and BI roles; star/snowflake schema terms appear frequently in job postings and vendor docs for Snowflake/BigQuery/Redshift.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
10
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Modeling and Schema Design Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Modeling and Schema Design
data-modeling-and-schema-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Data Mapping Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: data mapping id=2487 · data-mapping

Aliases — catalog

  • data mapping (CANONICAL) primary

Context tags (catalog)

API integration ETL data cleansing data governance data integration data lakes data lineage data migration data modeling data profiling data quality data transformation data visualization data warehouse mapping rules mapping tools metadata management schema mapping

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Data Mapping
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Data mapping appears broadly in ETL/ELT, integration, and MDM job descriptions across BI and cloud data stacks; vendors like Informatica, dbt, and Azure Data Factory all center it as a core capability.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3239
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • External System Integrations Catalog dimension db id 14

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
External System Integrations
external-system-integrations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Processing 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
Project Management 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
Soft Skills
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Automation 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
DevOps Tools
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
Change Data Capture in_db
Batch Ingestion and Replication
batch-ingestion-and-replication
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Dimensional Data Model new
Data Modeling and Schema Design
data-modeling-and-schema-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Mapping in_db
External System Integrations
external-system-integrations
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 IBM DataStage | type=Data Engineering Tools subtype=ETL Tools nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Integration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Database Replication | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed SOA | type=Architecture Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed ESB | type=Architecture Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Normalized Data Model | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Processing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Project Management | type=Soft Skills subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Automation | type=DevOps Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Dimensional Data Model ↔ Data Modeling and Schema Design
nano JD Parser — gpt-4.1-nano click to toggle
RoleBusiness Intelligence Developer
CompanyNisum
Experience7 to 10 years in the Information Technology
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Nisum is a leading global",
      "last_5_words": "digital and physical channels."
    },
    "text": "Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto \u201c Building Success Together\u00ae ,\u201d Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today\u2019s world, with immersive and seamless experiences across digital and physical channels.",
    "word_count": 84
  },
  "certifications": [],
  "company_name": "Nisum",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE/BSC - Computer Science (or related)",
      "raw": "Bachelor\u2019s / Master\u2019s degree in specific technical fields like computer science, math, statistics preferred, or equivalent practical experience.",
      "requirement": "preferred"
    }
  ],
  "experience": {
    "max": 10,
    "min": 7,
    "raw": "7 to 10 years in the Information Technology"
  },
  "job_locations": [],
  "role": "Business Intelligence Developer",
  "role_aliases": [
    "BI Developer",
    "ETL Developer",
    "Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 3,
      "heading": "What You\u0027ll Do",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Business Intelligence developer in",
        "last_5_words": "respect, open communication, and cross-functional expertise."
      },
      "text": "\u2022 Business Intelligence developer in ETL areas, contributing to the development and able to identify avenues for automation.\n\u2022 Implement best practices and drive adoption around coding, design, quality, and performance.\n\u2022 Work collaboratively with other teams to foster an environment of respect, open communication, and cross-functional expertise.",
      "word_count": 45
    },
    {
      "bullet_count": 4,
      "heading": "What You Know",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 The overall experience of 7",
        "last_5_words": "and ESB technologies and patterns."
      },
      "text": "\u2022 The overall experience of 7 to 10 years in the Information Technology.\n\u2022 Extensive hands-on experience in building ETL interfaces using IBM Datastage to aggregate, cleanse and migrate data across Data Warehousing systems using staged data processing techniques, patterns and best practices.\n\u2022 Strong experience in full life cycle management of capturing, versioning, and migrating various IBM Datastage ETL metadata including data mapping and other data integration artifacts (such as schedulers, scripts, etc.) across environments using vendor platforms such as Data Stage, or other equivalent tools (including open source) by establishing standards, guidelines and best practices.\n\u2022 Experience with both normalized and dimensional data models, hands-on knowledge of other data integration techniques such as database replication, change data capture (CDC) etc. and familiarity of SOA and ESB technologies and patterns.",
      "word_count": 104
    },
    {
      "bullet_count": 7,
      "heading": "Demonstrable Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Strong analytical skills with",
        "last_5_words": "tasks and deliverables."
      },
      "text": "\u2022 Strong analytical skills with the ability to analyze information identify and formulate solutions to problems. Provides more in-depth analysis with a high-level view of goals and end deliverables.\n\u2022 Complete work within a reasonable time frame under the supervision of a manager or team lead.\n\u2022 Plan and manage all aspects of the support function.\n\u2022 Extensive knowledge of and proven experience with data processing systems, and methods of developing, testing and moving solutions to implementation.\n\u2022 Strong knowledge in project management practices and ability to document processes and procedures as needed.\n\u2022 Work collaboratively with other support team members and independently on assigned tasks and deliverables with minimum supervision\n\u2022 Self-motivated, working closely and actively communicating with person to accomplish time-critical tasks and deliverables.",
      "word_count": 118
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "IBM DataStage"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    },
    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "Database Replication"
    },
    {
      "is_primary": true,
      "skill_name": "Change Data Capture"
    },
    {
      "is_primary": true,
      "skill_name": "SOA"
    },
    {
      "is_primary": true,
      "skill_name": "ESB"
    },
    {
      "is_primary": true,
      "skill_name": "Normalized Data Model"
    },
    {
      "is_primary": true,
      "skill_name": "Dimensional Data Model"
    },
    {
      "is_primary": true,
      "skill_name": "Data Mapping"
    },
    {
      "is_primary": true,
      "skill_name": "Data Processing"
    },
    {
      "is_primary": false,
      "skill_name": "Project Management"
    },
    {
      "is_primary": false,
      "skill_name": "Automation"
    }
  ],
  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "BI Developer",
      "ETL Developer",
      "Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Nisum is a leading global",
        "last_5_words": "digital and physical channels."
      },
      "text": "Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto \u201c Building Success Together\u00ae ,\u201d Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today\u2019s world, with immersive and seamless experiences across digital and physical channels.",
      "word_count": 84
    },
    "certifications": [],
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        "domain": "IT Services \u0026 Consulting"
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      "secondary": null
    },
    "education": [
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        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE/BSC - Computer Science (or related)",
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      }
    ],
    "experience": {
      "max": 10,
      "min": 7,
      "raw": "7 to 10 years in the Information Technology"
    },
    "job_locations": [],
    "role": "Business Intelligence Developer",
    "role_aliases": [
      "BI Developer",
      "ETL Developer",
      "Data Engineer"
    ],
    "role_archetype": "Data",
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}
API 2 — extract-details
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      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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          "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_final_skills": [
    "ETL",
    "IBM DataStage",
    "Data Warehousing",
    "Data Integration",
    "Database Replication",
    "Change Data Capture",
    "SOA",
    "ESB",
    "Normalized Data Model",
    "Dimensional Data Model",
    "Data Mapping",
    "Data Processing",
    "Project Management",
    "Automation"
  ],
  "input_llm_skills": [
    "ETL",
    "IBM DataStage",
    "Data Warehousing",
    "Data Integration",
    "Database Replication",
    "Change Data Capture",
    "SOA",
    "ESB",
    "Normalized Data Model",
    "Dimensional Data Model",
    "Data Mapping",
    "Data Processing",
    "Project Management",
    "Automation"
  ],
  "new_aliases_persisted": 0,
  "run_id": "0ff2002c-5f95-4558-a9e8-432257524c42",
  "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",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "IBM DataStage",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "ETL Tools",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
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        "merge_log": [],
        "placed": null,
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        "skill_id": "ibm-datastage",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehousing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
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          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
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        "merge_log": [],
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        "skill_id": "data-warehousing",
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        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "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": {
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          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
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          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
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        "skill_id": "data-integration",
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        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Database Replication",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
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        "relationships": null,
        "skill_id": "database-replication",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Change data capture",
          "alias_type": "CANONICAL",
          "id": 344,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Change data capture",
        "id": 140,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "change-data-capture",
        "sub_category_id": 102,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Batch Ingestion and Replication",
            "id": 29,
            "rationale": "Moving data from source systems into landing zones or warehouses on batch schedules. Covers file ingestion, CDC-style replication, incremental loads, and source-to-target synchronization.",
            "slug": "batch-ingestion-and-replication",
            "source": "db"
          },
          "input_skill": "Change Data Capture",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Change Data Capture",
      "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": "SOA",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Architecture Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
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        "relationships": null,
        "skill_id": "soa",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ESB",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Architecture 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": "esb",
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        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Normalized Data Model",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "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": "normalized-data-model",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Dimensional modeling",
          "alias_type": "CANONICAL",
          "id": 329,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Dimensional modeling",
        "id": 125,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "dimensional-modeling",
        "sub_category_id": 10,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Modeling and Schema Design",
            "id": 26,
            "rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
            "slug": "data-modeling-and-schema-design",
            "source": "db"
          },
          "input_skill": "Dimensional Data Model",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Dimensional Data Model",
      "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": [
        {
          "alias_text": "data mapping",
          "alias_type": "CANONICAL",
          "id": 3824,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "data mapping",
        "id": 2487,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-mapping",
        "sub_category_id": 3239,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "External System Integrations",
            "id": 14,
            "rationale": "Connecting backend services to third-party APIs and internal enterprise systems. This includes client libraries, webhooks, retries, data mapping, and integration failure handling.",
            "slug": "external-system-integrations",
            "source": "db"
          },
          "input_skill": "Data Mapping",
          "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": "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": "Drupal Dev",
              "id": 228,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "drupal-dev",
              "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": "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": "Data Mapping",
      "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 Processing",
      "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-processing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Project Management",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Soft Skills",
          "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": "project-management",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Automation",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "DevOps 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": "automation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "IBM DataStage",
    "Data Warehousing",
    "Data Integration",
    "Database Replication",
    "SOA",
    "ESB",
    "Normalized Data Model",
    "Data Processing",
    "Project Management",
    "Automation"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "ETL / ELT Developer",
    "id": 50,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on ETL development and DataStage-based data integration, metadata migration, and support of data processing systems, which best matches an ETL/ELT Developer.",
    "role_archetype": "Data",
    "slug": "etl-elt-developer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "IBM DataStage",
      "tag": "new"
    },
    {
      "skill": "Data Warehousing",
      "tag": "new"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "Database Replication",
      "tag": "new"
    },
    {
      "skill": "Change Data Capture",
      "tag": "in_db"
    },
    {
      "skill": "SOA",
      "tag": "new"
    },
    {
      "skill": "ESB",
      "tag": "new"
    },
    {
      "skill": "Normalized Data Model",
      "tag": "new"
    },
    {
      "skill": "Dimensional Data Model",
      "tag": "in_db"
    },
    {
      "skill": "Data Mapping",
      "tag": "in_db"
    },
    {
      "skill": "Data Processing",
      "tag": "new"
    },
    {
      "skill": "Project Management",
      "tag": "new"
    },
    {
      "skill": "Automation",
      "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": 50,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Batch Ingestion and Replication",
          "id": 29,
          "rationale": "Moving data from source systems into landing zones or warehouses on batch schedules. Covers file ingestion, CDC-style replication, incremental loads, and source-to-target synchronization.",
          "slug": "batch-ingestion-and-replication",
          "source": "db"
        },
        "dimension_id": 29,
        "input_skill": "Change Data Capture",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 140,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 50,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Modeling and Schema Design",
          "id": 26,
          "rationale": "Designing curated data structures for analytics and downstream consumption. Covers dimensional modeling, normalization tradeoffs, slowly changing dimensions, and schema evolution for durable datasets.",
          "slug": "data-modeling-and-schema-design",
          "source": "db"
        },
        "dimension_id": 26,
        "input_skill": "Dimensional Data Model",
        "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": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 50,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "External System Integrations",
          "id": 14,
          "rationale": "Connecting backend services to third-party APIs and internal enterprise systems. This includes client libraries, webhooks, retries, data mapping, and integration failure handling.",
          "slug": "external-system-integrations",
          "source": "db"
        },
        "dimension_id": 14,
        "input_skill": "Data Mapping",
        "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",
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          },
          {
            "display_name": "Backend Developer",
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            "slug": "backend-engineer",
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          },
          {
            "display_name": "Drupal Dev",
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          },
          {
            "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",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
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            "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
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            "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2487,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "0ff2002c-5f95-4558-a9e8-432257524c42"
}

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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