← Back to history

Pipeline run

f6b1e0dd-e64e-4e34-939e-4201471fc920

Pipeline LLM cost (USD)
API 1: $0.0040 API 2: $0.0005 API 3: $0.0000 Total: $0.0045

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 ETL/data warehouse pipelines in Informatica, SQL, UNIX/Shell and DB2/Oracle; handle impact analysis, unit/QA testing, production support, and documentation while enforcing data quality, lineage, and compliance.
"Design and Build ETL solutions"
Tech stack maturity
Mainstream Legacy
Informatica and SQL are established enterprise data-engineering tools, and unit-testing indicates solid engineering practice within a traditional, widely used stack.
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 (20)
Informatica UNIX Shell Scripting SQL DB2 Oracle Autosys ETL Unit Testing QA Testing Performance Tuning Data Quality Metadata Data Lineage Data Warehouse Data Pipelines Data Integration Data Modeling Data Analysis Reporting
Skill cluster (3 dimension groups, role-scoped)
ETL and ELT Tooling
Informatica
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
UNIX Shell Scripting DB2 Oracle Autosys ETL Unit Testing QA Testing Performance Tuning Data Quality Metadata Data Lineage Data Warehouse Data Pipelines Data Integration Data Modeling Data Analysis Reporting
Show KRA description ↓
Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverables Build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting Review and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factors Oversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis Resolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirements Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals Understand the Business Requirements and perform impact Analysis Design and Build ETL solutions Perform Unit testing and support QA testing Performance tuning of jobs Support Production implementation Create documentation as per the organization policy At least 8+ years IT experience with primary focus and technical skills/experience in the following: Good experience on Informatica. UNIX and Shell Scripting. Advanced SQL Knowledge. Hands on experience on DB2/Oracle. Good experience in handling very large database (VLDB) Good experience in Autosys or any Batch monitoring tool. Strong communication skills, problem solving, and organizational skills Experience with working with 'data quality' concepts Experience with working with metadata and data lineage concepts Support project experience is an added advantage. Should have good knowledge on Design and Build ETL solutions

Signals

Skill data-engineer
0.11
Alias data-engineer
1.00
KRA data-engineer
0.64

Post-classification

Centroidupdated · n=111
Alias collision log
New-role queue
New skills captured17
New KRA captured

Captured for admin review

UNIX primary Data Engineer pending
Shell Scripting primary Data Engineer pending
DB2 primary Data Engineer pending
Oracle primary Data Engineer pending
Autosys primary Data Engineer pending
ETL primary Data Engineer pending
QA Testing primary Data Engineer pending
Performance Tuning primary Data Engineer pending
Data Quality primary Data Engineer pending
Metadata primary Data Engineer pending
Data Lineage primary Data Engineer pending
Data Warehouse primary Data Engineer pending
Data Pipelines primary Data Engineer pending
Data Integration primary Data Engineer pending
Data Modeling primary Data Engineer pending
Data Analysis Data Engineer pending
Reporting Data Engineer pending
Status: completed Created: 2026-05-27T14:00:42.646515Z Updated: 2026-05-27T14:02:37.919287Z API 3 duration: 15108 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
3
Skipped

Job description

About This Role

Wells Fargo is seeking a Senior Data Engineer...

In This Role, You Will
Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverablesBuild and maintain optimized and highly available data pipelines that facilitate deeper analysis and reportingReview and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factorsOversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysisResolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirementsCollaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goalsRequired Qualifications, US:
4+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, educationRequired Qualifications, International:
Experience in Data Engineering, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, educationDesired Qualifications:
 At least 8+ years IT experience with primary focus and technical skills/experience in the following:  Good experience on Informatica.  UNIX and Shell Scripting.  Advanced SQL Knowledge.  Hands on experience on DB2/Oracle.  Good experience in handling very large database (VLDB)  Good experience in Autosys or any Batch monitoring tool.  Strong communication skills, problem solving, and organizational skills  Experience with working with 'data quality' concepts  Experience with working with metadata and data lineage concepts  Support project experience is an added advantage.  Should have good knowledge on Design and Build ETL solutions Job Expectations:
 Understand the Business Requirements and perform impact Analysis  Design and Build ETL solutions  Perform Unit testing and support QA testing  Performance tuning of jobs  Support Production implementation  Create documentation as per the organization policy  About the Role

This role is to develop ETL solutions for managing the data repository for Brokerage applications.

Job Responsibilities
 Understand the Business Requirements and perform impact Analysis  Design and Build ETL solutions  Perform Unit testing and support QA testing  Performance tuning of jobs  Support Production implementation  Create documentation as per the organization policy Essential Qualifications

At least 8+ years IT experience with primary focus and technical skills/experience in the following:
 Good experience on Informatica.  UNIX and Shell Scripting.  Advanced SQL Knowledge.  Hands on experience on DB2/Oracle.  Good experience in handling very large database (VLDB)  Good experience in Autosys or any Batch monitoring tool.  Strong communication skills, problem solving, and organizational skills  Experience with working with 'data quality' concepts  Experience with working with metadata and data lineage concepts  Support project experience is an added advantage.  Should have good knowledge on Design and Build ETL solutions Desired Qualifications
 Strong application Development/Maintenance experience  User support and communication skills  Should have good onsite-offshore experience  Must be a good independent contributor to the team  We Value Diversity

At Wells Fargo, we believe in diversity, equity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in US: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Reference Number

R-132000-1

Skills from this JD

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

Informatica Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Informatica id=117 · informatica

Aliases — catalog

  • Informatica (CANONICAL) primary

Context tags (catalog)

CDC Cloud Data Integration ELT ETL IICS PowerCenter data integration data quality data warehousing lookup transformation mapping repository session source qualifier workflow

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Informatica
License
proprietary
Year introduced
1993
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Informatica appears frequently in enterprise data-integration and ETL job postings, especially alongside cloud migration and MDM roles; it remains a common hiring keyword rather than a sunset technology.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
114
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
UNIX 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
Operating Systems
Sub-category
general
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Shell Scripting 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
Scripting Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
DB2 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Oracle 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
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Autosys 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
Job Scheduling Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Unit Testing Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Unit Testing id=517 · unit-testing

Aliases — catalog

  • Unit Testing (CANONICAL)

Context tags (catalog)

JUnit NUnit TDD arrange-act-assert assertions code coverage fixtures mocking pytest regression stubs test cases test doubles test runner xUnit

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Testing Methodology
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Unit testing is a standard hiring requirement across software JDs and appears in mainstream frameworks/docs; GitHub and Stack Overflow usage remain consistently high, with no successor replacing it.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
44
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)
QA Testing 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
Testing Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Performance Tuning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: query tuning id=3553 · query-tuning

Aliases — catalog

  • query tuning (CANONICAL) primary
  • Query Tuning (CANONICAL)

Context tags (catalog)

SQL SQL optimization caching cost estimation cost-based optimization data retrieval database indexing database optimization database performance database profiling database statistics database tuning execution plan execution time indexing join strategies load balancing parameter sniffing query analysis query complexity query execution query performance query profiling query rewriting resource allocation resource utilization statistics

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Query Optimization
Confidence
0.82
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3067
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
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 Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Metadata 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 Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Lineage 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 Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehouse 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 Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Pipelines 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
Data Integration Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Visualization Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

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

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Informatica in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Unit Testing in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Performance Tuning new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
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

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed UNIX | type=Operating Systems subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed Shell Scripting | type=Scripting Languages subtype=general nature=LANGUAGE lifespan=EVERGREEN
canonical_skill_proposed DB2 | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Autosys | type=Job Scheduling Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed QA Testing | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Metadata | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Lineage | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouse | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Pipelines | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Integration | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Analysis | type=Data Analysis Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Reporting | type=Data Visualization Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Performance Tuning ↔ Relational Database Design
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
RoleSenior Data Engineer
CompanyWells Fargo
Experience4+ years of Data Engineering experience
DomainFinancial Services
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": "Wells Fargo",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "FinTech",
        "Banking"
      ],
      "domain": "Financial Services"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 4,
    "raw": "4+ years of Data Engineering experience"
  },
  "job_locations": [],
  "role": "Senior Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Senior Data Engineer",
    "Lead Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 6,
      "heading": "In This Role, You Will",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Lead moderately complex initiatives within",
        "last_5_words": "resolve data engineering issues and achieve"
      },
      "text": "Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverables\nBuild and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting\nReview and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factors\nOversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis\nResolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirements\nCollaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
      "word_count": 104
    },
    {
      "bullet_count": 6,
      "heading": "Job Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Understand the Business Requirements and",
        "last_5_words": "as per the organization policy"
      },
      "text": "Understand the Business Requirements and perform impact Analysis\nDesign and Build ETL solutions\nPerform Unit testing and support QA testing\nPerformance tuning of jobs\nSupport Production implementation\nCreate documentation as per the organization policy",
      "word_count": 30
    },
    {
      "bullet_count": 10,
      "heading": "Essential Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "At least 8+ years IT experience",
        "last_5_words": "knowledge on Design and Build ETL solutions"
      },
      "text": "At least 8+ years IT experience with primary focus and technical skills/experience in the following:\nGood experience on Informatica.\nUNIX and Shell Scripting.\nAdvanced SQL Knowledge.\nHands on experience on DB2/Oracle.\nGood experience in handling very large database (VLDB)\nGood experience in Autosys or any Batch monitoring tool.\nStrong communication skills, problem solving, and organizational skills\nExperience with working with \u0027data quality\u0027 concepts\nExperience with working with metadata and data lineage concepts\nSupport project experience is an added advantage.\nShould have good knowledge on Design and Build ETL solutions",
      "word_count": 92
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Informatica"
    },
    {
      "is_primary": true,
      "skill_name": "UNIX"
    },
    {
      "is_primary": true,
      "skill_name": "Shell Scripting"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "DB2"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": true,
      "skill_name": "Autosys"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Unit Testing"
    },
    {
      "is_primary": true,
      "skill_name": "QA Testing"
    },
    {
      "is_primary": true,
      "skill_name": "Performance Tuning"
    },
    {
      "is_primary": true,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": true,
      "skill_name": "Metadata"
    },
    {
      "is_primary": true,
      "skill_name": "Data Lineage"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehouse"
    },
    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": false,
      "skill_name": "Data Analysis"
    },
    {
      "is_primary": false,
      "skill_name": "Reporting"
    }
  ],
  "jd_role": {
    "display_name": "Senior Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Senior Data Engineer",
      "Lead Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": "Wells Fargo",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "FinTech",
          "Banking"
        ],
        "domain": "Financial Services"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 4,
      "raw": "4+ years of Data Engineering experience"
    },
    "job_locations": [],
    "role": "Senior Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Senior Data Engineer",
      "Lead Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 6,
        "heading": "In This Role, You Will",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Lead moderately complex initiatives within",
          "last_5_words": "resolve data engineering issues and achieve"
        },
        "text": "Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverables\nBuild and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting\nReview and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factors\nOversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis\nResolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirements\nCollaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
        "word_count": 104
      },
      {
        "bullet_count": 6,
        "heading": "Job Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Understand the Business Requirements and",
          "last_5_words": "as per the organization policy"
        },
        "text": "Understand the Business Requirements and perform impact Analysis\nDesign and Build ETL solutions\nPerform Unit testing and support QA testing\nPerformance tuning of jobs\nSupport Production implementation\nCreate documentation as per the organization policy",
        "word_count": 30
      },
      {
        "bullet_count": 10,
        "heading": "Essential Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "At least 8+ years IT experience",
          "last_5_words": "knowledge on Design and Build ETL solutions"
        },
        "text": "At least 8+ years IT experience with primary focus and technical skills/experience in the following:\nGood experience on Informatica.\nUNIX and Shell Scripting.\nAdvanced SQL Knowledge.\nHands on experience on DB2/Oracle.\nGood experience in handling very large database (VLDB)\nGood experience in Autosys or any Batch monitoring tool.\nStrong communication skills, problem solving, and organizational skills\nExperience with working with \u0027data quality\u0027 concepts\nExperience with working with metadata and data lineage concepts\nSupport project experience is an added advantage.\nShould have good knowledge on Design and Build ETL solutions",
        "word_count": 92
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "f6b1e0dd-e64e-4e34-939e-4201471fc920",
  "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": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
            "sentence": "Build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting",
            "similarity": 0.6657
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Oversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis",
            "similarity": 0.6518
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
            "similarity": 0.6069
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6415,
        "slug": "data-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": "Build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting",
            "similarity": 0.5211
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
            "similarity": 0.5022
          },
          {
            "kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
            "sentence": "Good experience in Autosys or any Batch monitoring tool.",
            "similarity": 0.4725
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.4986,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "collaborate with design, product, and backend teams",
            "sentence": "Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
            "similarity": 0.5868
          },
          {
            "kra_text": "integrate external APIs and data sources",
            "sentence": "Oversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis",
            "similarity": 0.46
          },
          {
            "kra_text": "translate product and design requirements",
            "sentence": "Understand the Business Requirements and perform impact Analysis",
            "similarity": 0.4471
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 74,
        "score": 0.498,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverables",
            "similarity": 0.5013
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Resolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirements",
            "similarity": 0.4991
          },
          {
            "kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
            "sentence": "Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals",
            "similarity": 0.4903
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.4969,
        "slug": "cloud-architect",
        "total_count": null
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": [
          {
            "kra_text": "Requirements analysis and process translation",
            "sentence": "Understand the Business Requirements and perform impact Analysis",
            "similarity": 0.569
          },
          {
            "kra_text": "flow, rule, and integration testing",
            "sentence": "Perform Unit testing and support QA testing",
            "similarity": 0.468
          },
          {
            "kra_text": "Requirements analysis and process translation",
            "sentence": "Review and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factors",
            "similarity": 0.4361
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 24,
        "score": 0.4911,
        "slug": "pega-developer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Informatica",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.1111,
        "slug": "data-engineer",
        "total_count": 18
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.0556,
        "slug": "pega-developer",
        "total_count": 18
      }
    ]
  },
  "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": 111,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 6344,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "UNIX",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6345,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Shell Scripting",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6346,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "DB2",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6347,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Oracle",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6348,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Autosys",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6349,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6350,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "QA Testing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6351,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Performance Tuning",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6352,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Quality",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6353,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Metadata",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6354,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Lineage",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6355,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Warehouse",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6356,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6357,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Integration",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 6358,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 6359,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Analysis",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 6360,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Reporting",
        "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": 311,
      "existing_alias_text": "Informatica",
      "input_term": "Informatica",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Informatica",
        "id": 117,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "informatica",
        "sub_category_id": 114,
        "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": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "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": 865,
      "existing_alias_text": "Unit Testing",
      "input_term": "Unit Testing",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "Unit Testing",
        "id": 517,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "unit-testing",
        "sub_category_id": 44,
        "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": 5584,
      "existing_alias_text": "Query Tuning",
      "input_term": "Performance Tuning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "query tuning",
        "id": 3553,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "query-tuning",
        "sub_category_id": 3067,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_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": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
      "id": 79,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "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": "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"
      },
      "input_skill": "Informatica",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Pega Programming Languages \u0026 DSLs",
        "id": 267,
        "rationale": "Programming languages and domain-specific languages used in Pega development.",
        "slug": "pega-programming-languages-dsls",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Pega Developer",
          "id": 24,
          "rationale": null,
          "role_archetype": null,
          "slug": "pega-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Unit Testing",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Relational Database Design",
        "id": 4,
        "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
        "slug": "relational-database-design",
        "source": "db"
      },
      "input_skill": "Performance Tuning",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
          "id": 84,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
          "id": 85,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
          "id": 87,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Data Modeling",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Data Modeling",
      "llm_role": null,
      "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"
        }
      ]
    }
  ],
  "input_final_skills": [
    "Informatica",
    "UNIX",
    "Shell Scripting",
    "SQL",
    "DB2",
    "Oracle",
    "Autosys",
    "ETL",
    "Unit Testing",
    "QA Testing",
    "Performance Tuning",
    "Data Quality",
    "Metadata",
    "Data Lineage",
    "Data Warehouse",
    "Data Pipelines",
    "Data Integration",
    "Data Modeling",
    "Data Analysis",
    "Reporting"
  ],
  "input_llm_skills": [
    "Informatica",
    "UNIX",
    "Shell Scripting",
    "SQL",
    "DB2",
    "Oracle",
    "Autosys",
    "ETL",
    "Unit Testing",
    "QA Testing",
    "Performance Tuning",
    "Data Quality",
    "Metadata",
    "Data Lineage",
    "Data Warehouse",
    "Data Pipelines",
    "Data Integration",
    "Data Modeling",
    "Data Analysis",
    "Reporting"
  ],
  "new_aliases_persisted": 0,
  "run_id": "f6b1e0dd-e64e-4e34-939e-4201471fc920",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Informatica",
          "alias_type": "CANONICAL",
          "id": 311,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Informatica",
        "id": 117,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "informatica",
        "sub_category_id": 114,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Informatica",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Informatica",
      "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": "UNIX",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Operating Systems",
          "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": "unix",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Shell Scripting",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Scripting Languages",
          "skill_nature": "LANGUAGE",
          "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": "shell-scripting",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "SQL",
          "alias_type": "CANONICAL",
          "id": 271,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Pega Programming Languages \u0026 DSLs",
            "id": 267,
            "rationale": "Programming languages and domain-specific languages used in Pega development.",
            "slug": "pega-programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for Data Work",
            "id": 21,
            "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
            "slug": "programming-languages-for-data-work",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "SQL",
      "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": "DB2",
      "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": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "db2",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Oracle",
      "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": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "oracle",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Autosys",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Job Scheduling Tools",
          "skill_nature": "TOOL",
          "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": "autosys",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "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": "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": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Unit Testing",
          "alias_type": "CANONICAL",
          "id": 865,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Unit Testing",
        "id": 517,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "unit-testing",
        "sub_category_id": 44,
        "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": "Unit Testing",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Unit Testing",
      "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": "QA Testing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Testing 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": "qa-testing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "query tuning",
          "alias_type": "CANONICAL",
          "id": 5119,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Query Tuning",
          "alias_type": "CANONICAL",
          "id": 5584,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "query tuning",
        "id": 3553,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "query-tuning",
        "sub_category_id": 3067,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "Performance Tuning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Performance Tuning",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Quality",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "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
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Metadata",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "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": "metadata",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Lineage",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "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-lineage",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Warehouse",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "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-warehouse",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Pipelines",
      "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-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Integration",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "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-integration",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "domain modeling",
          "alias_type": "CANONICAL",
          "id": 3675,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Domain Modeling",
          "alias_type": "CANONICAL",
          "id": 5644,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "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"
            }
          ]
        }
      ],
      "input_skill": "Data Modeling",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Analysis",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Analysis 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-analysis",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Reporting",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Visualization 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": "reporting",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "UNIX",
    "Shell Scripting",
    "DB2",
    "Oracle",
    "Autosys",
    "ETL",
    "QA Testing",
    "Data Quality",
    "Metadata",
    "Data Lineage",
    "Data Warehouse",
    "Data Pipelines",
    "Data Integration",
    "Data Analysis",
    "Reporting"
  ]
}
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": "Informatica",
      "tag": "in_db"
    },
    {
      "skill": "UNIX",
      "tag": "new"
    },
    {
      "skill": "Shell Scripting",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "DB2",
      "tag": "new"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "Autosys",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Unit Testing",
      "tag": "in_db"
    },
    {
      "skill": "QA Testing",
      "tag": "new"
    },
    {
      "skill": "Performance Tuning",
      "tag": "in_db"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "Metadata",
      "tag": "new"
    },
    {
      "skill": "Data Lineage",
      "tag": "new"
    },
    {
      "skill": "Data Warehouse",
      "tag": "new"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Data Integration",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Analysis",
      "tag": "new"
    },
    {
      "skill": "Reporting",
      "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": "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": "Informatica",
        "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": 117,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "SQL",
        "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": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "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": 101,
        "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": "Unit Testing",
        "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": 517,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Database Design",
          "id": 4,
          "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
          "slug": "relational-database-design",
          "source": "db"
        },
        "dimension_id": 4,
        "input_skill": "Performance Tuning",
        "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": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": 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": "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"
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 3
  },
  "planner_output": null,
  "run_id": "f6b1e0dd-e64e-4e34-939e-4201471fc920"
}

LLM Calls

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

Loading…