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Pipeline run

4fcfcd8a-d4b2-41cf-ba2d-28ba01ef3ae8

Pipeline LLM cost (USD)
API 1: $0.0033 API 2: $0.0002 API 3: $0.0000 Total: $0.0035

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Build and tune Snowflake ELT and dbt workflows, model data for analytics/reporting, and enforce performance, quality, CI/CD, and privacy best practices while guiding a data engineering team.
"Lead data modeling efforts to support analytics and reporting needs across the organization."
Tech stack maturity
Modern Cloud Native
The stack centers on Snowflake and dbt with CI/CD and code review practices, which are characteristic of a modern cloud-native data engineering workflow.
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 (10)
Snowflake dbt ELT Data Modeling CI/CD Version Control Data Quality GDPR CCPA Code Review
Skill cluster (4 dimension groups, role-scoped)
Cloud Data Warehouses
Snowflake
Compliance and Security Frameworks
GDPR
ETL and ELT Tooling
dbt
Cross-cutting / unaligned
ELT Data Modeling CI/CD Version Control Data Quality CCPA Code Review
Show KRA description ↓
• Design and implement scalable and efficient Snowflake data warehouse architectures and ELT pipelines. • Leverage DBT to build and manage data transformation workflows within Snowflake. • Lead data modeling efforts to support analytics and reporting needs across the organization. • Optimize Snowflake performance including query tuning, resource scaling, and storage usage. • Collaborate with business stakeholders and data analysts to gather requirements and deliver high-quality data solutions. • Manage and mentor a team of data engineers; provide technical guidance, code reviews, and career development support. • Establish and enforce best practices for data engineering, including version control, CI/CD, documentation, and data quality. • Ensure data solutions are secure, compliant, and aligned with privacy regulations (e.g., GDPR, CCPA). • Continuously evaluate emerging tools and technologies to enhance our data ecosystem.

Signals

Skill data-engineer
0.20
Alias data-engineer
1.00
KRA data-engineer
0.71

Post-classification

Centroidupdated · n=399
Alias collision log
New-role queue
New skills captured5
New KRA captured

Captured for admin review

ELT primary Data Engineer pending
Data Modeling primary Data Engineer pending
Version Control primary Data Engineer pending
Data Quality primary Data Engineer pending
CCPA primary Data Engineer pending
Status: completed Created: 2026-05-27T16:07:52.052417Z Updated: 2026-05-27T16:09:25.067021Z API 3 duration: 19265 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 0.20 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
2
Skipped

Job description

Experience: 2.00 + years

Salary: INR 3000000-3500000 / year (based on experience)

Expected Notice Period: 30 Days

Shift: (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type: Office (Hyderabad)

Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: Blend360)

(*Note: This is a requirement for one of Uplers' client - Blend360)

What do you need for this opportunity?

Must have skills required:

Problem Solving Skills, Python, dbt, MySQL, Snowflake

Blend360 is Looking for:

We are seeking a Lead Snowflake Engineer to join our dynamic Data Engineering team. This role will involve owning the architecture, implementation, and optimization of our Snowflake-based data warehouse solutions while mentoring a team of engineers and driving project success. The ideal candidate will bring deep technical expertise in Snowflake, hands-on experience with DBT (Data Build Tool), and a collaborative mindset for working across data, analytics, and business teams.

Key Responsibilities:

• Design and implement scalable and efficient Snowflake data warehouse architectures and ELT pipelines.
• Leverage DBT to build and manage data transformation workflows within Snowflake.
• Lead data modeling efforts to support analytics and reporting needs across the organization.
• Optimize Snowflake performance including query tuning, resource scaling, and storage usage.
• Collaborate with business stakeholders and data analysts to gather requirements and deliver high-quality data solutions.
• Manage and mentor a team of data engineers; provide technical guidance, code reviews, and career development support.
• Establish and enforce best practices for data engineering, including version control, CI/CD, documentation, and data quality.
• Ensure data solutions are secure, compliant, and aligned with privacy regulations (e.g., GDPR, CCPA).
• Continuously evaluate emerging tools and technologies to enhance our data ecosystem.



Qualifications :

• Bachelor’s degree in Computer Science, Information Technology, or related field.
• 6+ years of experience in data engineering, including at least 2+ years of hands-on experience with Snowflake.
• Strong experience with DBT for managing ELT transformations in a modern data stack.
• Proficiency in SQL, Python, and experience working with large-scale data environments.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Demonstrated experience leading engineering teams and managing end-to-end project delivery.
• Strong understanding of data architecture, warehousing concepts, and dimensional modeling.
• Excellent problem-solving, communication, and collaboration skills.
• Familiarity with data governance, security, and privacy standards.



Interview Process

R1: Tech screening, python sql

R2: design/architecture/data arch

R3: deets pending

R4: Project presentation

How to apply for this opportunity?

• Step 1: Click On Apply! And Register or Login on our portal.
• Step 2: Complete the Screening Form & Upload updated Resume
• Step 3: Increase your chances to get shortlisted & meet the client for the Interview!



About Uplers:

Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.

(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).

So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
dbt Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: dbt id=115 · dbt

Aliases — catalog

  • dbt (CANONICAL) primary

Context tags (catalog)

BigQuery Databricks ELT Jinja Redshift SQL Snowflake YAML data modeling incremental models macros snapshots sources tests warehouse

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Analytics Engineering Framework
Vendor
dbt Labs
License
apache_2
Year introduced
2016
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: dbt appears in many analytics engineer and data platform job descriptions, and its GitHub repo has strong adoption signals with widespread ecosystem support from major cloud/data vendors.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
89
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
ELT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Domain Modeling
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
2831
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Ci Cd Process
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version 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
Software Development
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
GDPR Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GDPR id=402 · gdpr

Aliases — catalog

  • GDPR (CANONICAL) primary

Context tags (catalog)

DPA DPIA DPO RoPA SCCs accountability audit trail compliance consent consent management controller cross-border transfers data breach data breach notification data minimization data portability data privacy data processing data processing agreement data protection data protection officer data subject data subject rights encryption lawful basis personal data privacy by design privacy impact assessment privacy policy processor records of processing regulatory framework right to access right to erasure third-party vendors

Stored enrichment (catalog DB)

Category
Standard
Sub-category
Privacy Regulation Standard
Vendor
European Union
Year introduced
2016
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GDPR is a widely cited compliance requirement in job postings for product, legal, security, and data roles across EU-facing companies; it remains an active regulatory standard rather than a niche tool.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
3215
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Compliance and Security Frameworks Catalog dimension db id 73

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Standards, Protocols & Compliance Catalog dimension db id 452

    Library dimension (catalog)

    Roles linked in library: Engineering Manager, Sitecore Dev

  • Web Standards & Compliance Catalog dimension db id 343

    Library dimension (catalog)

    Roles linked in library: WordPress Dev

  • Web Standards, Protocols & Compliance Catalog dimension db id 436

    Library dimension (catalog)

    Roles linked in library: Shopify Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Web Standards & Compliance
web-standards-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Web Standards, Protocols & Compliance
web-standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CCPA 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
Compliance
Sub-category
general
Skill nature
CREDENTIAL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Code Review Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Code Review id=516 · code-review

Aliases — catalog

  • Code Review (CANONICAL)

Context tags (catalog)

Bitbucket GitHub GitLab PR review approval workflow branch protection code quality diff inline comments linting merge request pair programming pull request review checklist static analysis

Stored enrichment (catalog DB)

Category
SoftSkill
Sub-category
Code Review
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
58
Sub-category id
364
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)

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
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
dbt in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
CI/CD in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Web Standards & Compliance
web-standards-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GDPR in_db
Web Standards, Protocols & Compliance
web-standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Code Review in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Version Control | type=Software Development subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed CCPA | type=Compliance subtype=general nature=CREDENTIAL lifespan=SHORT_LIVED
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleLead Snowflake Engineer
CompanyUplers
Experience6+ years of experience in data engineering, including at least 2+ years of hands-on experience with Snowflake.
CTC{'max': 3500000, 'min': 3000000, 'raw': 'INR 3000000-3500000 / year', 'period': 'annual', 'currency': 'INR'}
DomainIT Services & Consulting
Location Hyderabad, India (onsite)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Our goal is to make",
      "last_5_words": "you may face during the engagement."
    },
    "text": "Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.",
    "word_count": 45
  },
  "certifications": [],
  "company_name": "Uplers",
  "ctc": {
    "currency": "INR",
    "max": 3500000,
    "min": 3000000,
    "period": "annual",
    "raw": "INR 3000000-3500000 / year"
  },
  "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 degree in Computer Science, Information Technology, or related field.",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 6,
    "raw": "6+ years of experience in data engineering, including at least 2+ years of hands-on experience with Snowflake."
  },
  "job_locations": [
    {
      "aliases": [
        "Hyderabad, AP"
      ],
      "city": "Hyderabad",
      "country": "India",
      "state": null,
      "work_mode": "onsite"
    }
  ],
  "role": "Lead Snowflake Engineer",
  "role_aliases": [
    "Snowflake Engineer",
    "Data Engineer",
    "Lead Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 9,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Design and implement scalable",
        "last_5_words": "enhance our data ecosystem."
      },
      "text": "\u2022 Design and implement scalable and efficient Snowflake data warehouse architectures and ELT pipelines.\n\u2022 Leverage DBT to build and manage data transformation workflows within Snowflake.\n\u2022 Lead data modeling efforts to support analytics and reporting needs across the organization.\n\u2022 Optimize Snowflake performance including query tuning, resource scaling, and storage usage.\n\u2022 Collaborate with business stakeholders and data analysts to gather requirements and deliver high-quality data solutions.\n\u2022 Manage and mentor a team of data engineers; provide technical guidance, code reviews, and career development support.\n\u2022 Establish and enforce best practices for data engineering, including version control, CI/CD, documentation, and data quality.\n\u2022 Ensure data solutions are secure, compliant, and aligned with privacy regulations (e.g., GDPR, CCPA).\n\u2022 Continuously evaluate emerging tools and technologies to enhance our data ecosystem.",
      "word_count": 174
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": true,
      "skill_name": "dbt"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": true,
      "skill_name": "Version Control"
    },
    {
      "is_primary": true,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": true,
      "skill_name": "GDPR"
    },
    {
      "is_primary": true,
      "skill_name": "CCPA"
    },
    {
      "is_primary": true,
      "skill_name": "Code Review"
    }
  ],
  "jd_role": {
    "display_name": "Lead Snowflake Engineer",
    "rationale": null,
    "role_aliases": [
      "Snowflake Engineer",
      "Data Engineer",
      "Lead Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "Our goal is to make",
        "last_5_words": "you may face during the engagement."
      },
      "text": "Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.",
      "word_count": 45
    },
    "certifications": [],
    "company_name": "Uplers",
    "ctc": {
      "currency": "INR",
      "max": 3500000,
      "min": 3000000,
      "period": "annual",
      "raw": "INR 3000000-3500000 / year"
    },
    "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 degree in Computer Science, Information Technology, or related field.",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 6,
      "raw": "6+ years of experience in data engineering, including at least 2+ years of hands-on experience with Snowflake."
    },
    "job_locations": [
      {
        "aliases": [
          "Hyderabad, AP"
        ],
        "city": "Hyderabad",
        "country": "India",
        "state": null,
        "work_mode": "onsite"
      }
    ],
    "role": "Lead Snowflake Engineer",
    "role_aliases": [
      "Snowflake Engineer",
      "Data Engineer",
      "Lead Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 9,
        "heading": "Key Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Design and implement scalable",
          "last_5_words": "enhance our data ecosystem."
        },
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    "urls": []
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  "rejected": false,
  "rejection_reason": null,
  "run_id": "4fcfcd8a-d4b2-41cf-ba2d-28ba01ef3ae8",
  "stage3_signals": {
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        "matched_count": null,
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    ],
    "kra_match_roles": [
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      },
      {
        "display_name": "Cloud Architect",
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          },
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        "role_id": 9,
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      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
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      },
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        "display_name": "Cloud Security Engineer",
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        ],
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    ],
    "skill_match_roles": [
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          "Snowflake",
          "dbt"
        ],
        "role_id": 2,
        "score": 0.2,
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      },
      {
        "display_name": "ML Engineer",
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          "CI/CD"
        ],
        "role_id": 3,
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      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 1,
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          "GDPR"
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        "role_id": 5,
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      },
      {
        "display_name": "DevOps Engineer",
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          "CI/CD"
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        "score": 0.1,
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        "total_count": 10
      },
      {
        "display_name": "Cloud Security Engineer",
        "kra_matches": null,
        "matched_count": 1,
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          "GDPR"
        ],
        "role_id": 23,
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        "total_count": 10
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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      "role_id": 2,
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      "slug": "data-engineer",
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    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
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    "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 0.20 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 399,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 18437,
        "role_display_name": "Data Engineer",
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        "skill_name": "ELT",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18438,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18439,
        "role_display_name": "Data Engineer",
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        "skill_name": "Version Control",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18440,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Quality",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18441,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "CCPA",
        "status": "pending"
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    ],
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    "v3_pipeline_triggered": false,
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  }
}
API 2 — extract-details
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "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": 309,
      "existing_alias_text": "dbt",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "sub_category_id": 89,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
      "input_term": "Data Modeling",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1826,
      "existing_alias_text": "CI/CD",
      "input_term": "CI/CD",
      "matched_canonical": {
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        "display_name": "CI/CD",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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        "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": 718,
      "existing_alias_text": "GDPR",
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      "matched_canonical": {
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        "display_name": "GDPR",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
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      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 864,
      "existing_alias_text": "Code Review",
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      "matched_canonical": {
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        "display_name": "Code Review",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PRACTICE",
        "slug": "code-review",
        "sub_category_id": 364,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
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  ],
  "candidate_roles": [
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    },
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      "source": "db"
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      "rationale": null,
      "role_archetype": "Engineering",
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    },
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      "display_name": "PHP Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
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    },
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      "rationale": null,
      "role_archetype": "Engineering",
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    },
    {
      "display_name": "Sitecore Dev",
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    {
      "display_name": "WordPress Dev",
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      "role_archetype": "Engineering",
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      "role_archetype": "Engineering",
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  ],
  "chosen_role": {
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    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.20 does not contradict",
    "role_archetype": null,
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      "dimension": {
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      "input_skill": "Snowflake",
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      "roles_from_db": [
        {
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      ]
    },
    {
      "dimension": {
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        "source": "db"
      },
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    {
      "dimension": {
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        },
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "CI/CD Pipeline Platforms",
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        "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
        "slug": "ci-cd-pipeline-platforms",
        "source": "db"
      },
      "input_skill": "CI/CD",
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      "roles_from_db": [
        {
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  ],
  "unmatched_skills": [
    "ELT",
    "Version Control",
    "Data Quality",
    "CCPA"
  ]
}
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 0.20 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "dbt",
      "tag": "in_db"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "Version Control",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "GDPR",
      "tag": "in_db"
    },
    {
      "skill": "CCPA",
      "tag": "new"
    },
    {
      "skill": "Code Review",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Snowflake",
        "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": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "dbt",
        "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": 115,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
          "source": "db"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "CI/CD",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "CI/CD",
        "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": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Compliance and Security Frameworks",
          "id": 73,
          "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
          "slug": "compliance-and-security-frameworks",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "GDPR",
        "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": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Standards, Protocols \u0026 Compliance",
          "id": 452,
          "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
          "slug": "standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 452,
        "input_skill": "GDPR",
        "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": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Standards \u0026 Compliance",
          "id": 343,
          "rationale": "Ensuring WordPress sites adhere to web markup, styling, accessibility, and privacy regulations.",
          "slug": "web-standards-compliance",
          "source": "db"
        },
        "dimension_id": 343,
        "input_skill": "GDPR",
        "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": "WordPress Dev",
            "id": 227,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "wordpress-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Standards, Protocols \u0026 Compliance",
          "id": 436,
          "rationale": "Adhering to industry standards and regulatory compliance when designing and integrating storefront solutions.",
          "slug": "web-standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 436,
        "input_skill": "GDPR",
        "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": "Shopify Dev",
            "id": 230,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "shopify-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 402,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Code Review",
        "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": 516,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 2
  },
  "planner_output": null,
  "run_id": "4fcfcd8a-d4b2-41cf-ba2d-28ba01ef3ae8"
}