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Pipeline run

f81487ca-532e-4f5f-ba28-89ef80544cd0

Pipeline LLM cost (USD)
API 1: $0.0037 API 2: $0.0003 API 3: $0.0000 Total: $0.0040

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and secure ETL data pipelines that ingest logs, databases, and APIs, enforce encryption/masking/access controls, and monitor/repair data flow and quality for security teams.
"“Design and build scalable data pipelines to collect, process, and analyze large volumes of security-related data.”"
Tech stack maturity
Mainstream Modern
APIs and data-masking are common, widely adopted capabilities in contemporary data engineering stacks, typically implemented with modern but not cutting-edge tooling.
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 (9)
ETL APIs Databases Data Encryption Data Masking Access Control Data Validation Data Monitoring Data Quality
Skill cluster (2 dimension groups, role-scoped)
Data Governance and Access Controls
Data Masking
Cross-cutting / unaligned
ETL APIs Databases Data Encryption Access Control Data Validation Data Monitoring Data Quality
Show KRA description ↓
Data Pipeline Development: • Design and build scalable data pipelines to collect, process, and analyze large volumes of security-related data. • Implement ETL (Extract, Transform, Load) processes to ensure data integrity and quality. Data Security • Ensure data pipelines and storage solutions comply with security standards and best practices. • Implement data encryption, masking, and access control mechanisms. Integration • Integrate data from various sources, including logs, databases, and external APIs. • Ensure seamless data flow between systems and applications. Collaboration • Work closely with data scientists and security analysts to understand data requirements and deliver solutions. • Collaborate with IT and cybersecurity teams to ensure data infrastructure aligns with security needs. Performance Optimization • Monitor and optimize the performance of data pipelines and systems. • Troubleshoot and resolve data processing issues. Data Quality • Implement data validation and monitoring to ensure data accuracy and consistency. • Develop and enforce data quality standards and best practices. Documentation • Maintain comprehensive documentation of data pipelines, processes, and security measures. • Prepare reports on data processing and security status. Technology Evaluation • Evaluate and recommend new tools and technologies to enhance data processing and security capabilities. • Stay updated with the latest trends and advancements in data engineering and cybersecurity.

Signals

Skill data-engineer
0.11
Alias data-engineer
1.00
KRA data-engineer
0.70

Post-classification

Centroidupdated · n=435
Alias collision log
New-role queue
New skills captured7
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
Databases primary Data Engineer pending
Data Encryption primary Data Engineer pending
Access Control primary Data Engineer pending
Data Validation primary Data Engineer pending
Data Monitoring primary Data Engineer pending
Data Quality primary Data Engineer pending
Status: completed Created: 2026-05-27T16:28:13.419279Z Updated: 2026-05-27T16:29:42.608050Z API 3 duration: 13406 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 A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.11 does not contradict

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
0
Skipped

Job description

Skills:
Cybersecurity Data Engineer, Data Security, Python Java Scala, Database systems data warehousing, Data Integration, Data Pipeline,

Greetings from Netsach - A Cyber Security Company.

Position Overview: The Data Engineer will be responsible for designing, developing, and maintaining data pipelines and systems to ensure secure, reliable, and efficient data processing. This role involves working closely with data scientists, security analysts, and other stakeholders to support our cybersecurity initiatives.

Job Title: Data Engineer

Exp: 7yrs

Location: Bangalore Onsite

Interested candidates please share your updated resume at emily@netsach.co.in. Looking for immediate joiners /15 days.

Key Responsibilities

Data Pipeline Development:

• Design and build scalable data pipelines to collect, process, and analyze large volumes of security-related data.
• Implement ETL (Extract, Transform, Load) processes to ensure data integrity and quality.


Data Security

• Ensure data pipelines and storage solutions comply with security standards and best practices.
• Implement data encryption, masking, and access control mechanisms.


Integration

• Integrate data from various sources, including logs, databases, and external APIs.
• Ensure seamless data flow between systems and applications.


Collaboration

• Work closely with data scientists and security analysts to understand data requirements and deliver solutions.
• Collaborate with IT and cybersecurity teams to ensure data infrastructure aligns with security needs.


Performance Optimization

• Monitor and optimize the performance of data pipelines and systems.
• Troubleshoot and resolve data processing issues.


Data Quality

• Implement data validation and monitoring to ensure data accuracy and consistency.
• Develop and enforce data quality standards and best practices.


Documentation

• Maintain comprehensive documentation of data pipelines, processes, and security measures.
• Prepare reports on data processing and security status.


Technology Evaluation

• Evaluate and recommend new tools and technologies to enhance data processing and security capabilities.
• Stay updated with the latest trends and advancements in data engineering and cybersecurity.


Qualifications

• Education:
• Bachelors degree in Computer Science, Information Technology, Cyber Security, Data Engineering, or a related field.
• Experience:
• Proven experience in data engineering, with a focus on building and managing data pipelines.
• Strong background in cybersecurity or a related field is preferred.
• Skills:
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with data processing frameworks such as Apache Spark, Hadoop, or similar.
• Knowledge of database systems (SQL, NoSQL) and data warehousing solutions.
• Understanding of data security principles and best practices.
• Excellent problem-solving, analytical, and communication skills.
• Certifications:
• Relevant certifications such as CDP, CISSP, or equivalent are a plus.


Thank You

Emily Jha

emily@netsach.co.in

Netsach - A Cyber Security Company

www.netsach.co.in

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
APIs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: APIs id=1192 · apis

Aliases — catalog

  • APIs (CANONICAL)

Context tags (catalog)

API Gateway Endpoint GraphQL JSON JWT Microservices OAuth Postman REST Rate Limiting SOAP Swagger Throttling Webhooks XML

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Application Programming Interfaces
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
902
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Databases 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
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Data Encryption 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Masking Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Data masking id=155 · data-masking

Aliases — catalog

  • Data masking (CANONICAL) primary

Context tags (catalog)

PHI PII access controls anonymization data classification de-identification dynamic data masking field-level masking format-preserving encryption obfuscation pseudonymization redaction sensitive data static data masking tokenization

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Data Protection Concept
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in security/compliance job descriptions and vendor docs for PCI/GDPR; often paired with DLP and tokenization in enterprise data platforms.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
77
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Governance and Access Controls Catalog dimension db id 32

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Governance and Access Controls
data-governance-and-access-controls
Existing dimension (library) · Role↔dimension saved
Access Control 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Validation 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 Monitoring 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
Monitoring Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Quality 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

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
APIs in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Masking in_db
Data Governance and Access Controls
data-governance-and-access-controls
Existing dimension (library) · Role↔dimension saved

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 Databases | type=Databases subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed Data Encryption | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Access Control | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Validation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Monitoring | type=Monitoring Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanyNetsach
Experience7yrs
DomainCybersecurity
Location Bangalore, India (onsite)
JD type pass

Certifications

CDP CISSP
Show raw JSON
{
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  "about_company": {
    "source_marker": {
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      "last_5_words": "Cyber Security Company"
    },
    "text": "Netsach - A Cyber Security Company",
    "word_count": 7
  },
  "certifications": [
    "CDP",
    "CISSP"
  ],
  "company_name": "Netsach",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
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  "education": [
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      "requirement": "required"
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  "experience": {
    "max": null,
    "min": 7,
    "raw": "7yrs"
  },
  "job_locations": [
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      "city": "Bangalore",
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      "state": null,
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    }
  ],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Cybersecurity Data Engineer",
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  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 15,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Data Pipeline Development:\n\n\u2022 Design and",
        "last_5_words": "data engineering and cybersecurity."
      },
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      "word_count": 366
    }
  ],
  "urls": [
    {
      "type": "website",
      "url": "http://www.netsach.co.in"
    }
  ]
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "APIs"
    },
    {
      "is_primary": true,
      "skill_name": "Databases"
    },
    {
      "is_primary": true,
      "skill_name": "Data Encryption"
    },
    {
      "is_primary": true,
      "skill_name": "Data Masking"
    },
    {
      "is_primary": true,
      "skill_name": "Access Control"
    },
    {
      "is_primary": true,
      "skill_name": "Data Validation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Monitoring"
    },
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  "jd_role": {
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    "slug": ""
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  "nano_parsed": {
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      },
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    "certifications": [
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    "ctc": null,
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    "experience": {
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    "urls": [
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  "rejected": false,
  "rejection_reason": null,
  "run_id": "f81487ca-532e-4f5f-ba28-89ef80544cd0",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "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
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
            "sentence": "Implement data validation and monitoring to ensure data accuracy and consistency.",
            "similarity": 0.7227
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Work closely with data scientists and security analysts to understand data requirements and deliver solutions.",
            "similarity": 0.6929
          },
          {
            "kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
            "sentence": "Monitor and optimize the performance of data pipelines and systems.",
            "similarity": 0.672
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6958,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Integrate data from various sources, including logs, databases, and external APIs.",
            "similarity": 0.7684
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Collaborate with IT and cybersecurity teams to ensure data infrastructure aligns with security needs.",
            "similarity": 0.5047
          },
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Ensure seamless data flow between systems and applications.",
            "similarity": 0.429
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.5674,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": [
          {
            "kra_text": "Defines secure engineering standards, secure coding guidelines, threat intelligence feeds, and compliance requirements for the organization.",
            "sentence": "Ensure data pipelines and storage solutions comply with security standards and best practices.",
            "similarity": 0.5782
          },
          {
            "kra_text": "Conducts security posture assessments, vulnerability scans, and penetration testing to identify weaknesses and evaluate overall system security.",
            "sentence": "Prepare reports on data processing and security status.",
            "similarity": 0.5635
          },
          {
            "kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
            "sentence": "Evaluate and recommend new tools and technologies to enhance data processing and security capabilities.",
            "similarity": 0.5396
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 5,
        "score": 0.5604,
        "slug": "cybersecurity-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Monitor and optimize the performance of data pipelines and systems.",
            "similarity": 0.6469
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Collaborate with IT and cybersecurity teams to ensure data infrastructure aligns with security needs.",
            "similarity": 0.4967
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Maintain comprehensive documentation of data pipelines, processes, and security measures.",
            "similarity": 0.476
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.5399,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
            "sentence": "Monitor and optimize the performance of data pipelines and systems.",
            "similarity": 0.5605
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Implement data validation and monitoring to ensure data accuracy and consistency.",
            "similarity": 0.5299
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "Develop and enforce data quality standards and best practices.",
            "similarity": 0.5138
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.5348,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Data masking"
        ],
        "role_id": 2,
        "score": 0.1111,
        "slug": "data-engineer",
        "total_count": 9
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "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": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.11 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 435,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 20201,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20202,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Databases",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20203,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Encryption",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20204,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Access Control",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20205,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Validation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20206,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Monitoring",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 20207,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Quality",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1828,
      "existing_alias_text": "APIs",
      "input_term": "APIs",
      "matched_canonical": {
        "category_id": 10,
        "display_name": "APIs",
        "id": 1192,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "apis",
        "sub_category_id": 902,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 364,
      "existing_alias_text": "Data masking",
      "input_term": "Data Masking",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Data masking",
        "id": 155,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-masking",
        "sub_category_id": 77,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.11 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "APIs",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Data Governance and Access Controls",
        "id": 32,
        "rationale": "Controls and policies for protecting sensitive data and managing who can see or use it. This includes classification, masking, row-level access, retention, and auditability for governed datasets.",
        "slug": "data-governance-and-access-controls",
        "source": "db"
      },
      "input_skill": "Data Masking",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    }
  ],
  "input_final_skills": [
    "ETL",
    "APIs",
    "Databases",
    "Data Encryption",
    "Data Masking",
    "Access Control",
    "Data Validation",
    "Data Monitoring",
    "Data Quality"
  ],
  "input_llm_skills": [
    "ETL",
    "APIs",
    "Databases",
    "Data Encryption",
    "Data Masking",
    "Access Control",
    "Data Validation",
    "Data Monitoring",
    "Data Quality"
  ],
  "new_aliases_persisted": 0,
  "run_id": "f81487ca-532e-4f5f-ba28-89ef80544cd0",
  "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": [
        {
          "alias_text": "APIs",
          "alias_type": "CANONICAL",
          "id": 1828,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 10,
        "display_name": "APIs",
        "id": 1192,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "apis",
        "sub_category_id": 902,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "APIs",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "APIs",
      "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": "Databases",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "databases",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Encryption",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-encryption",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Data masking",
          "alias_type": "CANONICAL",
          "id": 364,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Data masking",
        "id": 155,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-masking",
        "sub_category_id": 77,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Governance and Access Controls",
            "id": 32,
            "rationale": "Controls and policies for protecting sensitive data and managing who can see or use it. This includes classification, masking, row-level access, retention, and auditability for governed datasets.",
            "slug": "data-governance-and-access-controls",
            "source": "db"
          },
          "input_skill": "Data Masking",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Data Masking",
      "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": "Access Control",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "access-control",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Validation",
      "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-validation",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Monitoring",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Monitoring 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-monitoring",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Quality",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-quality",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Databases",
    "Data Encryption",
    "Access Control",
    "Data Validation",
    "Data Monitoring",
    "Data Quality"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.11 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "APIs",
      "tag": "in_db"
    },
    {
      "skill": "Databases",
      "tag": "new"
    },
    {
      "skill": "Data Encryption",
      "tag": "new"
    },
    {
      "skill": "Data Masking",
      "tag": "in_db"
    },
    {
      "skill": "Access Control",
      "tag": "new"
    },
    {
      "skill": "Data Validation",
      "tag": "new"
    },
    {
      "skill": "Data Monitoring",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "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": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "APIs",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1192,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Governance and Access Controls",
          "id": 32,
          "rationale": "Controls and policies for protecting sensitive data and managing who can see or use it. This includes classification, masking, row-level access, retention, and auditability for governed datasets.",
          "slug": "data-governance-and-access-controls",
          "source": "db"
        },
        "dimension_id": 32,
        "input_skill": "Data Masking",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 155,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "f81487ca-532e-4f5f-ba28-89ef80544cd0"
}