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Pipeline run

fd82bb2f-3b97-439b-9ce8-1c83d77d7ede

Pipeline LLM cost (USD)
API 1: $0.0091 API 2: $0.0005 API 3: $0.0000 Total: $0.0096

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Data Engineering / BI
Design and optimize Redshift data models and ETL/ELT pipelines, then build Power BI dashboards and analytics workflows that turn business questions into KPIs, insights, and executive-ready reporting.
"“Lead the design, development, and optimization of scalable data models using Redshift”"
Tech stack maturity
Mainstream Modern
Amazon Redshift, Power BI, and SQL are widely adopted contemporary analytics technologies that fit a mainstream modern data 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 (25)
Amazon Redshift SQL ETL ELT Data Modeling Data Warehousing Power BI DAX AWS Glue dbt Apache Airflow Fivetran Star Schema Snowflake Schema Metadata Management Python HubSpot Salesforce Mixpanel Google Analytics 4 Amplitude SaaS B2B Data Security Privacy Standards
Skill cluster (2 dimension groups, role-scoped)
Python Programming
Python
Cross-cutting / unaligned
Amazon Redshift SQL ETL ELT Data Modeling Data Warehousing Power BI DAX AWS Glue dbt Apache Airflow Fivetran Star Schema Snowflake Schema Metadata Management HubSpot Salesforce Mixpanel Google Analytics 4 Amplitude SaaS B2B Data Security Privacy Standards
Show KRA description ↓
• Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses). • Build and maintain robust ETL/ELT data pipelines that ensure data quality, availability, and reliability. • Own and enhance dashboards and analytics workflows in Power BI and other BI tools. • Translate business problems into analytical requirements and deliver insights that drive measurable outcomes. • Partner with cross-functional teams to define KPIs, reporting frameworks, and success metrics. • Conduct exploratory data analysis to identify trends, patterns, revenue opportunities, and performance gaps. • Ensure strong governance, documentation, and best practices in data operations. • Mentor junior analysts and contribute to building a data-driven culture across the organization. • 5+ years of experience in data analytics, BI, or data engineering roles. • Hands-on expertise with Amazon Redshift, including SQL performance optimization and data modeling. • Strong experience with Power BI — DAX, visuals, dataflows, and dashboard publishing. • Proven track record in building ETL/ELT pipelines, preferably using tools like AWS Glue, DBT, Airflow, Fivetran, or similar. • Strong understanding of data warehousing concepts, star/snowflake schema, and metadata management. • Proficient in advanced SQL for large-scale analytical workloads. • Ability to convert complex business requirements into insightful dashboards and actionable intelligence. • Experience with statistical analysis and data storytelling to executive stakeholders. • Experience in SaaS or B2B enterprise tech businesses. • Familiarity with CRM (HubSpot/Salesforce), marketing automation, product analytics tools (Mixpanel, GA4, Amplitude). • Knowledge of Python for analytics automation and data manipulation. • Exposure to data security, privacy standards, and compliance practices. • Someone who is curious, detail-oriented, and loves solving business problems with data. • A strong communicator who can simplify complexity for non-technical stakeholders. • Collaborative and proactive — bringing ideas, not just reports.

Signals

Skill data-engineer
0.38
Alias data-analyst
1.00
KRA data-engineer
0.64

Post-classification

Centroidupdated · n=6
Alias collision log
New-role queue
New skills captured15
New KRA capturedyes

Captured for admin review

ETL primary Data Analyst pending
ELT primary Data Analyst pending
Data Modeling primary Data Analyst pending
Data Warehousing primary Data Analyst pending
DAX primary Data Analyst pending
AWS Glue Data Analyst pending
Snowflake Schema Data Analyst pending
HubSpot Data Analyst pending
Mixpanel Data Analyst pending
Google Analytics 4 Data Analyst pending
Amplitude Data Analyst pending
SaaS Data Analyst pending
B2B Data Analyst pending
Data Security Data Analyst pending
Privacy Standards Data Analyst pending
R&R fragment (sim 0.00) Data Analyst pending

• Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses). • Build and maintain robust ETL/ELT data pipelines that ensure data quality, a…

Status: completed Created: 2026-05-27T16:38:07.905124Z Updated: 2026-05-27T16:40:30.618699Z API 3 duration: 43078 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 Analyst

domain · Data Engineering & Analytics CASE DOMAIN

slug: data-analyst · id: 143 · source: db

Domain=Data Engineering & Analytics; The role is primarily analytics-focused with dashboarding, KPI definition, exploratory analysis, and stakeholder insights, which best matches Senior Data Analyst rather than a pure BI or engineering role.

Matched skills

RedshiftPower BIETL/ELTAWS GlueDBTAirflowFivetranSQLstar/snowflake schemametadata managementPythonHubSpotSalesforceMixpanelGA4Amplitude

Matched dimensions

Data AnalyticsBI DashboardingData ModelingData Pipeline SupportKPI and Metrics DefinitionExploratory Data AnalysisData Governance and DocumentationStakeholder Communication

Matched KRAs

Lead the design, development, and optimization of scalable data modelsBuild and maintain robust ETL/ELT data pipelinesOwn and enhance dashboards and analytics workflowsTranslate business problems into analytical requirementsPartner with cross-functional teams to define KPIsConduct exploratory data analysis to identify trendsEnsure strong governance, documentation, and best practicesMentor junior analysts and build a data-driven culture

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

About The Role

We are looking for a highly skilled Senior Data Analyst who can own our analytics stack end-to-end. This role blends technical expertise in data modelling, pipelines, and reporting with strong business acumen to convert raw data into actionable insights. You will collaborate closely with leadership, product, sales, marketing, and customer teams to drive better decisions and improve product and business performance.

Key Responsibilities

• Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses).
• Build and maintain robust ETL/ELT data pipelines that ensure data quality, availability, and reliability.
• Own and enhance dashboards and analytics workflows in Power BI and other BI tools.
• Translate business problems into analytical requirements and deliver insights that drive measurable outcomes.
• Partner with cross-functional teams to define KPIs, reporting frameworks, and success metrics.
• Conduct exploratory data analysis to identify trends, patterns, revenue opportunities, and performance gaps.
• Ensure strong governance, documentation, and best practices in data operations.
• Mentor junior analysts and contribute to building a data-driven culture across the organization.


Required Skills & Experience

• 5+ years of experience in data analytics, BI, or data engineering roles.
• Hands-on expertise with Amazon Redshift, including SQL performance optimization and data modeling.
• Strong experience with Power BI — DAX, visuals, dataflows, and dashboard publishing.
• Proven track record in building ETL/ELT pipelines, preferably using tools like AWS Glue, DBT, Airflow, Fivetran, or similar.
• Strong understanding of data warehousing concepts, star/snowflake schema, and metadata management.
• Proficient in advanced SQL for large-scale analytical workloads.
• Ability to convert complex business requirements into insightful dashboards and actionable intelligence.
• Experience with statistical analysis and data storytelling to executive stakeholders.


Preferred Qualifications

• Experience in SaaS or B2B enterprise tech businesses.
• Familiarity with CRM (HubSpot/Salesforce), marketing automation, product analytics tools (Mixpanel, GA4, Amplitude).
• Knowledge of Python for analytics automation and data manipulation.
• Exposure to data security, privacy standards, and compliance practices.


Who Will Succeed In This Role

• Someone who is curious, detail-oriented, and loves solving business problems with data.
• A strong communicator who can simplify complexity for non-technical stakeholders.
• Collaborative and proactive — bringing ideas, not just reports.


Why Join Us

• High ownership and impact — your insights directly shape product and revenue strategy.
• Opportunity to build and scale the analytics landscape in a high-growth environment.
• Cross-functional exposure to product, finance, sales, marketing, and leadership teams.

Skills from this JD

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

Amazon Redshift Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon Redshift id=107 · amazon-redshift

Aliases — catalog

  • Amazon Redshift (CANONICAL) primary

Context tags (catalog)

AWS Glue Amazon S3 BI COPY command ELT ETL JDBC ODBC RA3 SQL Spectrum analytics data warehouse distribution key sort key

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2012
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in data/analytics job descriptions and widely used as AWS’s managed warehouse; strong vendor adoption and steady JD volume signal broad market demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • 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 & DSLs
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 skipped (dimension not under chosen role)
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

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

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Data Warehousing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Power BI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Power BI id=151 · power-bi

Aliases — catalog

  • Power BI (CANONICAL) primary

Context tags (catalog)

Azure Synapse DAX DirectQuery Import mode M language Power Query RLS SQL Server SSAS dashboard data modeling data warehouse gateway reporting star schema

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Microsoft
License
proprietary
Year introduced
2015
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DAX 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
Programming Languages
Sub-category
general
Skill nature
LANGUAGE
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
AWS Glue 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
dbt Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: dbt id=115 · dbt

Aliases — catalog

  • dbt (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Airflow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Airflow id=110 · apache-airflow

Aliases — catalog

  • Apache Airflow (CANONICAL) primary

Context tags (catalog)

CeleryExecutor DAG ETL KubernetesExecutor Sensors XCom backfill catchup cron data pipelines executor hooks operators scheduler task dependencies

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2015
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Frequently listed in data engineering JDs and widely adopted for workflow orchestration; strong GitHub activity and managed offerings from AWS/GCP/Azure signal broad market demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Pipeline Orchestration Catalog dimension db id 23

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Fivetran Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Fivetran id=116 · fivetran

Aliases — catalog

  • Fivetran (CANONICAL) primary

Context tags (catalog)

API connector BigQuery CDC ELT Redshift SaaS sources Snowflake connector data ingestion data pipeline data warehouse dbt incremental sync replication schema drift

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Integration Platform
Vendor
Fivetran, Inc.
License
proprietary
Year introduced
2012
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in data engineering JDs and partner ecosystems; Fivetran’s broad connector catalog and frequent mentions alongside dbt/Snowflake signal mainstream adoption.

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 skipped (dimension not under chosen role)
Star Schema Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Star schema id=126 · star-schema

Aliases — catalog

  • Star schema (CANONICAL) primary

Context tags (catalog)

ETL Kimball OLAP aggregate table business intelligence conformed dimensions data mart denormalization dimension table fact table grain slowly changing dimension snowflake schema star join surrogate key

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Data Warehouse Architecture
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common data-warehouse pattern in BI/analytics JDs and vendor docs; widely used alongside Snowflake/BigQuery/Redshift for dimensional modeling, with no sunset or replacement signal.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
70
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Modeling and Schema Design Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake Schema Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Metadata Management Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Metadata management id=137 · metadata-management

Aliases — catalog

  • Metadata management (CANONICAL) primary

Context tags (catalog)

business glossary cataloging controlled vocabulary data catalog data classification data dictionary data governance data lineage data quality data stewardship master data management metadata repository ontology schema registry taxonomy

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Metadata Management Methodology
Confidence
0.92
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in data/platform JDs: roles for data governance, MDM, and cataloging routinely list metadata management alongside tools like Collibra/Alation and cloud data catalogs.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
109
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Lineage and Metadata Catalog dimension db id 28

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
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 skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HubSpot 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
Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Salesforce Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Salesforce id=4632 · salesforce

Aliases — catalog

  • Salesforce (CANONICAL) primary

Context tags (catalog)

API Integration Apex AppExchange Custom Objects Data Loader Einstein Analytics Lightning Process Builder SOQL SObject Sales Cloud Service Cloud Trailhead Visualforce Workflow Rules

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Saas Platform
Vendor
Salesforce, Inc.
License
proprietary
Year introduced
1999
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Salesforce appears in large volumes of enterprise job postings across sales, admin, and developer roles, and its AppExchange/ecosystem remains actively supported by the vendor.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
3633
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Mixpanel 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
Analytics Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Google Analytics 4 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
Analytics Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Amplitude 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
Analytics Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
SaaS 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
Platforms
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
B2B 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Security 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Privacy Standards 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

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

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Amazon Redshift in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 & DSLs
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 skipped (dimension not under chosen role)
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
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
dbt in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Airflow in_db
Data Pipeline Orchestration
data-pipeline-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Fivetran in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Star Schema in_db
Data Modeling and Schema Design
data-modeling-and-schema-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metadata Management in_db
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Salesforce in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed DAX | type=Programming Languages subtype=general nature=LANGUAGE lifespan=SHORT_LIVED
canonical_skill_proposed AWS Glue | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Snowflake Schema | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed HubSpot | type=Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Mixpanel | type=Analytics Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Google Analytics 4 | type=Analytics Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Amplitude | type=Analytics Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed SaaS | type=Platforms subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed B2B | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Security | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Privacy Standards | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior Data Analyst
Experience5+ years of experience in data analytics, BI, or data engineering roles.
DomainOther
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
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  "education": [],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "5+ years of experience in data analytics, BI, or data engineering roles."
  },
  "job_locations": [],
  "role": "Senior Data Analyst",
  "role_aliases": [
    "Data Analyst",
    "Senior Analyst",
    "BI Analyst"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
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      "bullet_count": 8,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Lead the design, development,",
        "last_5_words": "data-driven culture across the organization."
      },
      "text": "\u2022 Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses).\n\u2022 Build and maintain robust ETL/ELT data pipelines that ensure data quality, availability, and reliability.\n\u2022 Own and enhance dashboards and analytics workflows in Power BI and other BI tools.\n\u2022 Translate business problems into analytical requirements and deliver insights that drive measurable outcomes.\n\u2022 Partner with cross-functional teams to define KPIs, reporting frameworks, and success metrics.\n\u2022 Conduct exploratory data analysis to identify trends, patterns, revenue opportunities, and performance gaps.\n\u2022 Ensure strong governance, documentation, and best practices in data operations.\n\u2022 Mentor junior analysts and contribute to building a data-driven culture across the organization.",
      "word_count": 134
    },
    {
      "bullet_count": 8,
      "heading": "Required Skills \u0026 Experience",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 5+ years of experience in",
        "last_5_words": "data storytelling to executive stakeholders."
      },
      "text": "\u2022 5+ years of experience in data analytics, BI, or data engineering roles.\n\u2022 Hands-on expertise with Amazon Redshift, including SQL performance optimization and data modeling.\n\u2022 Strong experience with Power BI \u2014 DAX, visuals, dataflows, and dashboard publishing.\n\u2022 Proven track record in building ETL/ELT pipelines, preferably using tools like AWS Glue, DBT, Airflow, Fivetran, or similar.\n\u2022 Strong understanding of data warehousing concepts, star/snowflake schema, and metadata management.\n\u2022 Proficient in advanced SQL for large-scale analytical workloads.\n\u2022 Ability to convert complex business requirements into insightful dashboards and actionable intelligence.\n\u2022 Experience with statistical analysis and data storytelling to executive stakeholders.",
      "word_count": 139
    },
    {
      "bullet_count": 4,
      "heading": "Preferred Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Experience in SaaS or B2B",
        "last_5_words": "security, privacy standards, and compliance practices."
      },
      "text": "\u2022 Experience in SaaS or B2B enterprise tech businesses.\n\u2022 Familiarity with CRM (HubSpot/Salesforce), marketing automation, product analytics tools (Mixpanel, GA4, Amplitude).\n\u2022 Knowledge of Python for analytics automation and data manipulation.\n\u2022 Exposure to data security, privacy standards, and compliance practices.",
      "word_count": 56
    },
    {
      "bullet_count": 3,
      "heading": "Who Will Succeed In This Role",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Someone who is curious,",
        "last_5_words": "not just reports."
      },
      "text": "\u2022 Someone who is curious, detail-oriented, and loves solving business problems with data.\n\u2022 A strong communicator who can simplify complexity for non-technical stakeholders.\n\u2022 Collaborative and proactive \u2014 bringing ideas, not just reports.",
      "word_count": 36
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Amazon Redshift"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    },
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      "skill_name": "Power BI"
    },
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    },
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      "is_primary": false,
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    {
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      "skill_name": "Apache Airflow"
    },
    {
      "is_primary": false,
      "skill_name": "Fivetran"
    },
    {
      "is_primary": false,
      "skill_name": "Star Schema"
    },
    {
      "is_primary": false,
      "skill_name": "Snowflake Schema"
    },
    {
      "is_primary": false,
      "skill_name": "Metadata Management"
    },
    {
      "is_primary": false,
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    },
    {
      "is_primary": false,
      "skill_name": "HubSpot"
    },
    {
      "is_primary": false,
      "skill_name": "Salesforce"
    },
    {
      "is_primary": false,
      "skill_name": "Mixpanel"
    },
    {
      "is_primary": false,
      "skill_name": "Google Analytics 4"
    },
    {
      "is_primary": false,
      "skill_name": "Amplitude"
    },
    {
      "is_primary": false,
      "skill_name": "SaaS"
    },
    {
      "is_primary": false,
      "skill_name": "B2B"
    },
    {
      "is_primary": false,
      "skill_name": "Data Security"
    },
    {
      "is_primary": false,
      "skill_name": "Privacy Standards"
    }
  ],
  "jd_role": {
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        "word_count": 36
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  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Analyst",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 143,
        "score": 1.0,
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        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
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          {
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            "similarity": 0.6661
          },
          {
            "kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
            "sentence": "Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses).",
            "similarity": 0.6517
          },
          {
            "kra_text": "Maintains data catalog entries, column-level data lineage, and technical documentation to support data discoverability and governance across the organization.",
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            "similarity": 0.6139
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6439,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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            "similarity": 0.5725
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Hands-on expertise with Amazon Redshift, including SQL performance optimization and data modeling.",
            "similarity": 0.5277
          },
          {
            "kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
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            "similarity": 0.4334
          }
        ],
        "matched_count": null,
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        "role_id": 15,
        "score": 0.5112,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
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          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
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          },
          {
            "kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4927,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
          {
            "kra_text": "translate product and design requirements",
            "sentence": "Translate business problems into analytical requirements and deliver insights that drive measurable outcomes.",
            "similarity": 0.5513
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
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            "similarity": 0.4521
          },
          {
            "kra_text": "collaborate with design, product, and backend teams",
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          }
        ],
        "matched_count": null,
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        "role_id": 74,
        "score": 0.4844,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": [
          {
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            "similarity": 0.5211
          },
          {
            "kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
            "sentence": "Lead the design, development, and optimization of scalable data models using Redshift (or other cloud data warehouses).",
            "similarity": 0.5183
          },
          {
            "kra_text": "Configures Docker containers, deployment descriptors, environment variables, and CI/CD pipeline stages for backend service releases.",
            "sentence": "Build and maintain robust ETL/ELT data pipelines that ensure data quality, availability, and reliability.",
            "similarity": 0.3965
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 0.4786,
        "slug": "backend-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Amazon Redshift",
          "Power BI",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.375,
        "slug": "data-engineer",
        "total_count": 8
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.125,
        "slug": "pega-developer",
        "total_count": 8
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 121,
        "score": 0.125,
        "slug": "engineering-manager",
        "total_count": 8
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Analyst",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 143,
      "score": 0.96,
      "slug": "data-analyst",
      "total_count": null
    },
    "confidence": 0.96,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Data Analytics",
      "BI Dashboarding",
      "Data Modeling",
      "Data Pipeline Support",
      "KPI and Metrics Definition",
      "Exploratory Data Analysis",
      "Data Governance and Documentation",
      "Stakeholder Communication"
    ],
    "matched_kras": [
      "Lead the design, development, and optimization of scalable data models",
      "Build and maintain robust ETL/ELT data pipelines",
      "Own and enhance dashboards and analytics workflows",
      "Translate business problems into analytical requirements",
      "Partner with cross-functional teams to define KPIs",
      "Conduct exploratory data analysis to identify trends",
      "Ensure strong governance, documentation, and best practices",
      "Mentor junior analysts and build a data-driven culture"
    ],
    "matched_skills": [
      "Redshift",
      "Power BI",
      "ETL/ELT",
      "AWS Glue",
      "DBT",
      "Airflow",
      "Fivetran",
      "SQL",
      "star/snowflake schema",
      "metadata management",
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      "HubSpot",
      "Salesforce",
      "Mixpanel",
      "GA4",
      "Amplitude"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The role is primarily analytics-focused with dashboarding, KPI definition, exploratory analysis, and stakeholder insights, which best matches Senior Data Analyst rather than a pure BI or engineering role.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 6,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.0,
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      "role_slug": "data-analyst",
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    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 21067,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 21068,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "ELT",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 21069,
        "role_display_name": "Data Analyst",
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        "skill_name": "Data Modeling",
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      },
      {
        "is_primary": true,
        "queue_id": 21070,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Warehousing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 21071,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "DAX",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21072,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "AWS Glue",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21073,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Snowflake Schema",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21074,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "HubSpot",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21075,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Mixpanel",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21076,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Google Analytics 4",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21077,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Amplitude",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21078,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "SaaS",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21079,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "B2B",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21080,
        "role_display_name": "Data Analyst",
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        "skill_name": "Data Security",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 21081,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Privacy Standards",
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      }
    ],
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    "v3_pipeline_triggered": false,
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    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persisted": false,
      "existing_alias_id": 301,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-redshift",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    {
      "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": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 360,
      "existing_alias_text": "Power BI",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 309,
      "existing_alias_text": "dbt",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 304,
      "existing_alias_text": "Apache Airflow",
      "input_term": "Apache Airflow",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 310,
      "existing_alias_text": "Fivetran",
      "input_term": "Fivetran",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 330,
      "existing_alias_text": "Star schema",
      "input_term": "Star Schema",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 341,
      "existing_alias_text": "Metadata management",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
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        "display_name": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 6567,
      "existing_alias_text": "Salesforce",
      "input_term": "Salesforce",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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  ],
  "candidate_roles": [
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      "role_archetype": "Engineering",
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      "display_name": "Cyber Security Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
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    {
      "display_name": "ML Engineer",
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      "rationale": null,
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      "slug": "ml-engineer",
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    {
      "display_name": "MLOps Engineer",
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      "slug": "ml-ops-engineer",
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      "display_name": "AR/VR Engineer",
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      "role_archetype": null,
      "slug": "ar-vr-engineer",
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  "chosen_role": {
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    "rationale": "Domain=Data Engineering \u0026 Analytics; The role is primarily analytics-focused with dashboarding, KPI definition, exploratory analysis, and stakeholder insights, which best matches Senior Data Analyst rather than a pure BI or engineering role.",
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          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "hubspot",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Salesforce",
          "alias_type": "CANONICAL",
          "id": 6567,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Salesforce",
        "id": 4632,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "salesforce",
        "sub_category_id": 3633,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Vendor Product Families",
            "id": 477,
            "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
            "slug": "vendor-product-families",
            "source": "db"
          },
          "input_skill": "Salesforce",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Salesforce",
      "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": "Mixpanel",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Analytics 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": "mixpanel",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Google Analytics 4",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Analytics 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": "google-analytics-4",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Amplitude",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Analytics 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": "amplitude",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "SaaS",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Platforms",
          "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": "saas",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "B2B",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "b2b",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Security",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-security",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Privacy Standards",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "privacy-standards",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "ELT",
    "Data Warehousing",
    "DAX",
    "AWS Glue",
    "Snowflake Schema",
    "HubSpot",
    "Mixpanel",
    "Google Analytics 4",
    "Amplitude",
    "SaaS",
    "B2B",
    "Data Security",
    "Privacy Standards"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Analyst",
    "id": 143,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The role is primarily analytics-focused with dashboarding, KPI definition, exploratory analysis, and stakeholder insights, which best matches Senior Data Analyst rather than a pure BI or engineering role.",
    "role_archetype": null,
    "slug": "data-analyst",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Amazon Redshift",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Warehousing",
      "tag": "new"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "DAX",
      "tag": "new"
    },
    {
      "skill": "AWS Glue",
      "tag": "new"
    },
    {
      "skill": "dbt",
      "tag": "in_db"
    },
    {
      "skill": "Apache Airflow",
      "tag": "in_db"
    },
    {
      "skill": "Fivetran",
      "tag": "in_db"
    },
    {
      "skill": "Star Schema",
      "tag": "in_db"
    },
    {
      "skill": "Snowflake Schema",
      "tag": "new"
    },
    {
      "skill": "Metadata Management",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "HubSpot",
      "tag": "new"
    },
    {
      "skill": "Salesforce",
      "tag": "in_db"
    },
    {
      "skill": "Mixpanel",
      "tag": "new"
    },
    {
      "skill": "Google Analytics 4",
      "tag": "new"
    },
    {
      "skill": "Amplitude",
      "tag": "new"
    },
    {
      "skill": "SaaS",
      "tag": "new"
    },
    {
      "skill": "B2B",
      "tag": "new"
    },
    {
      "skill": "Data Security",
      "tag": "new"
    },
    {
      "skill": "Privacy Standards",
      "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": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Amazon Redshift",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 107,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "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": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "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": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "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": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "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": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "BI and Visualization Tools",
          "id": 31,
          "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
          "slug": "bi-and-visualization-tools",
          "source": "db"
        },
        "dimension_id": 31,
        "input_skill": "Power BI",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 151,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "dbt",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 115,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Pipeline Orchestration",
          "id": 23,
          "rationale": "Workflow engines that schedule, coordinate, and recover batch data jobs. This cluster covers dependency management, retries, backfills, sensors, and operational control of pipeline DAGs.",
          "slug": "data-pipeline-orchestration",
          "source": "db"
        },
        "dimension_id": 23,
        "input_skill": "Apache Airflow",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 110,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "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": "Fivetran",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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