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Pipeline run

4af88673-2c35-40e3-a6e5-e6eeb7167844

Pipeline LLM cost (USD)
API 1: $0.0092 API 2: $0.0004 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 Analysis / ETL
Analyze ETL and mixed-source datasets, clean/validate data, write SQL/Python queries and scripts, and build dashboards/reports that surface trends, anomalies, and migration needs for engineers and business stakeholders.
"“Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements.”"
Tech stack maturity
Mainstream Modern
Python, R, and SQL are standard, widely adopted data analytics tools that fit a mainstream modern stack rather than a legacy or bleeding-edge one.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.50 / 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): AI, Machine Learning, Artificial Intelligence
Evidence — skills matched in JD (20)
ETL SQL Python Excel R Tableau Power BI Looker AWS Redshift Google BigQuery Azure Synapse Machine Learning Data Visualization Data Analysis Data Cleansing Data Validation Data Preprocessing Data Quality Data Warehouses Databases
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Python Programming
Python
Cross-cutting / unaligned
ETL SQL Excel R Tableau Power BI Looker AWS Redshift Google BigQuery Azure Synapse Data Visualization Data Analysis Data Cleansing Data Validation Data Preprocessing Data Quality Data Warehouses Databases
Show KRA description ↓
• Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements. • Perform data cleansing, validation, and preprocessing to prepare structured and unstructured data for analysis. • Analyze a variety of sources to check data quality, integrity, and accuracy. Document findings clearly and communicate with the engineers and owners. • Develop and execute queries, scripts, and data manipulation tasks using SQL, Python, or other relevant tools. • Analyze large datasets to identify trends, patterns, and correlations, drawing meaningful conclusions that inform business decisions. • Create clear and concise data visualizations, dashboards, and reports to communicate findings effectively to stakeholders. • Collaborate with clients and cross-functional teams to gather and understand data requirements, translating them into actionable insights. • Work closely with Data Engineers, users, and Business Owners to ensure effective and complete communication about requirements. • Collaborate with Data Scientists and other analysts to support predictive modeling, machine learning, and statistical analysis. • Continuously monitor data quality and proactively identify anomalies or discrepancies, recommending corrective actions. • Stay up-to-date with industry trends, emerging technologies, and best practices to enhance analytical techniques. • Assist in the identification and implementation of process improvements to streamline data workflows and analysis. • 3 + years of proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python]. • 3+ years of experience supporting Software Engineering, Data Engineering, or Data Analytics projects. • Excellent analytical and problem-solving skills. • Undergraduate or Graduate degree preferred • Strong proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python]. • Experience with data visualization tools like [Tools - e.g., Tableau, Power BI, Looker]. • Experience with cloud data platforms such as [Platforms - e.g., AWS Redshift, Google BigQuery, Azure Synapse]. • Familiarity with ETL (Extract, Transform, Load) processes and tools. • Knowledge of machine learning techniques and tools. • Experience in a specific industry (e.g., financial services, healthcare, manufacturing) can be a plus. • Understanding of data governance and data privacy regulations. • Ability to query and manipulate databases and data warehouses. • Strong communication skills with the ability to explain complex data insights to non-technical stakeholders. • Detail-oriented with a commitment to accuracy.

Signals

Skill engineering-manager
0.40
Alias data-engineer
1.00
KRA data-engineer
0.68

Post-classification

Centroidupdated · n=5
Alias collision log
New-role queue
New skills captured13
New KRA capturedyes

Captured for admin review

ETL primary Data Analyst pending
Excel primary Data Analyst pending
AWS Redshift Data Analyst pending
Google BigQuery Data Analyst pending
Azure Synapse Data Analyst pending
Data Visualization Data Analyst pending
Data Analysis Data Analyst pending
Data Cleansing Data Analyst pending
Data Validation Data Analyst pending
Data Preprocessing Data Analyst pending
Data Quality Data Analyst pending
Data Warehouses Data Analyst pending
Databases Data Analyst pending
R&R fragment (sim 0.00) Data Analyst pending

• Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements. • Perform data cleansing, validation, and preprocessing to p…

Status: completed Created: 2026-05-27T16:34:11.120605Z Updated: 2026-05-27T16:36:01.354997Z API 3 duration: 25047 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 JD is centered on data analysis, cleansing, validation, visualization, and stakeholder communication rather than building ETL pipelines or data platforms.

Matched skills

ETLSQLPythonExcelRTableauPower BILookerAWS RedshiftGoogle BigQueryAzure Synapsemachine learning

Matched dimensions

Data AnalysisData Quality AssessmentData Visualization and ReportingStakeholder CommunicationPredictive Analytics SupportData Governance AwarenessProcess Improvement

Matched KRAs

Analyze ETL workloads to understand migration requirementsPerform data cleansing, validation, and preprocessingCheck data quality, integrity, and accuracyDevelop and execute queries, scripts, and data manipulation tasksAnalyze large datasets to identify trends, patterns, and correlationsCreate data visualizations, dashboards, and reportsTranslate data requirements into actionable insightsMonitor data quality and identify anomalies or discrepanciesAssist in the identification and implementation of process improvements

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

Req ID: 310527

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Digital Engineering Senior Engineer to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

Key Responsibilities:

• Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements. 
• Perform data cleansing, validation, and preprocessing to prepare structured and unstructured data for analysis. 
• Analyze a variety of sources to check data quality, integrity, and accuracy. Document findings clearly and communicate with the engineers and owners. 
• Develop and execute queries, scripts, and data manipulation tasks using SQL, Python, or other relevant tools. 
• Analyze large datasets to identify trends, patterns, and correlations, drawing meaningful conclusions that inform business decisions. 
• Create clear and concise data visualizations, dashboards, and reports to communicate findings effectively to stakeholders. 
• Collaborate with clients and cross-functional teams to gather and understand data requirements, translating them into actionable insights. 
• Work closely with Data Engineers, users, and Business Owners to ensure effective and complete communication about requirements. 
• Collaborate with Data Scientists and other analysts to support predictive modeling, machine learning, and statistical analysis. 
• Continuously monitor data quality and proactively identify anomalies or discrepancies, recommending corrective actions. 
• Stay up-to-date with industry trends, emerging technologies, and best practices to enhance analytical techniques. 
• Assist in the identification and implementation of process improvements to streamline data workflows and analysis.


Basic Qualifications:

• 3 + years of proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python]. 
• 3+ years of experience supporting Software Engineering, Data Engineering, or Data Analytics projects. 
• Excellent analytical and problem-solving skills. 
• Undergraduate or Graduate degree preferred


Preferred Skills:

• Strong proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python]. 
• Experience with data visualization tools like [Tools - e.g., Tableau, Power BI, Looker]. 
• Experience with cloud data platforms such as [Platforms - e.g., AWS Redshift, Google BigQuery, Azure Synapse]. 
• Familiarity with ETL (Extract, Transform, Load) processes and tools. 
• Knowledge of machine learning techniques and tools. 
• Experience in a specific industry (e.g., financial services, healthcare, manufacturing) can be a plus. 
• Understanding of data governance and data privacy regulations. 
• Ability to query and manipulate databases and data warehouses. 
• Strong communication skills with the ability to explain complex data insights to non-technical stakeholders. 
• Detail-oriented with a commitment to accuracy.


About NTT DATA

NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com

 NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us .   This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here . If you'd like more information on your EEO rights under the law, please click here . For Pay Transparency information, please click here .

Skills from this JD

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

ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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)
Python Primary 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)
Excel 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 Analysis Tools
Sub-category
general
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
R Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: R id=194 · r

Aliases — catalog

  • R (VERSION)
  • R 3 (VERSION)
  • R 3.x (VERSION)
  • R 4 (VERSION)
  • R 4.0 (VERSION)
  • R 4.1 (VERSION)
  • R 4.2 (VERSION)
  • R 4.3 (VERSION)
  • R 4.4 (VERSION)
  • R 4.x (VERSION)

Context tags (catalog)

Bioconductor CRAN R Markdown Shiny Tidyverse caret data.table dplyr ggplot2 glm lme4 lubridate rstan tidyr tidyverse

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
R Core Team
License
gpl_v2
Year introduced
1993
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
R 4.x

Maturity reasoning: R appears in many data science, statistics, and analytics job postings, and CRAN remains active with broad package usage across academia and industry.

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)

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Tableau id=150 · tableau

Aliases — catalog

  • Tableau (CANONICAL) primary

Context tags (catalog)

LOD expressions Tableau Cloud Tableau Desktop Tableau Prep Tableau Server actions calculated fields dashboards data blending data visualization extracts filters parameters published data sources workbooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Tableau Software
License
proprietary
Year introduced
2003
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform 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)
Power BI Secondary 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)
Looker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Looker id=152 · looker

Aliases — catalog

  • Looker (CANONICAL) primary

Context tags (catalog)

BigQuery Dimensions Explores LookML Measures PDT SQL Runner Snowflake dashboards data modeling derived table drill-down embedded analytics scheduled delivery tiles

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Google Cloud
License
proprietary
Year introduced
2012
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.

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)
AWS Redshift Secondary 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Google BigQuery Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: BigQuery id=106 · bigquery

Aliases — catalog

  • BigQuery (CANONICAL) primary

Context tags (catalog)

Cloud Storage Dataflow ELT ETL GCP Google Cloud Platform Looker Pub/Sub SQL Standard SQL clustered tables data warehouse dbt partitioned tables service account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Google
License
proprietary
Year introduced
2011
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.

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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Azure Synapse Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure Synapse Analytics id=108 · azure-synapse-analytics

Aliases — catalog

  • Azure Synapse Analytics (CANONICAL) primary

Context tags (catalog)

Apache Spark Azure Data Lake Storage Data Factory Delta Lake PolyBase SQL pools Spark pools Synapse Studio T-SQL dedicated SQL pool linked services notebooks pipelines serverless SQL pool workspace

Stored enrichment (catalog DB)

Category
Service
Sub-category
Analytics Service
Vendor
Microsoft
License
proprietary
Year introduced
2019
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in cloud data-platform JDs and Microsoft’s Azure analytics stack; often listed alongside Databricks/ADF for warehousing and ETL, indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
117
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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Machine Learning Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Visualization 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Data Cleansing 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 Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Validation 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 Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Preprocessing 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 Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Quality 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 Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehouses 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
Databases 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
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
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
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)
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)
R in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Looker in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Redshift new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Google BigQuery new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Azure Synapse new
Cloud Data Warehouses
cloud-data-warehouses
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Excel | type=Data Analysis Tools subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed Data Visualization | type=Data Analysis Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Analysis | type=Data Analysis Tools subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Data Cleansing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Validation | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Preprocessing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehouses | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Databases | type=Databases subtype=general nature=TOOL lifespan=EVERGREEN
dimension_skill_link_proposed AWS Redshift ↔ Cloud Data Warehouses
dimension_skill_link_proposed Google BigQuery ↔ Cloud Data Warehouses
dimension_skill_link_proposed Azure Synapse ↔ Cloud Data Warehouses
nano JD Parser — gpt-4.1-nano click to toggle
RoleDigital Engineering Senior Engineer
CompanyNTT DATA
Experience3 + years of proficiency in data analysis tools
DomainIT Services & Consulting
Location Bangalore, India (null)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "NTT DATA is a $30 billion",
      "last_5_words": "into the digital future."
    },
    "text": "NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R\u0026D to help organizations and society move confidently and sustainably into the digital future.",
    "word_count": 108
  },
  "certifications": [],
  "company_name": "NTT DATA",
  "ctc": null,
  "domain": {
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        "Technology Services"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Bachelor\u0027s/Graduate - Any Discipline",
      "raw": "Undergraduate or Graduate degree preferred",
      "requirement": "preferred"
    }
  ],
  "experience": {
    "max": null,
    "min": 3,
    "raw": "3 + years of proficiency in data analysis tools"
  },
  "job_locations": [
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      ],
      "city": "Bangalore",
      "country": "India",
      "state": "Karn\u0101taka",
      "work_mode": "null"
    }
  ],
  "role": "Digital Engineering Senior Engineer",
  "role_aliases": [
    "Senior Data Engineer",
    "Data Analyst",
    "Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 12,
      "heading": "Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Analyze ETL (Extract, transform,",
        "last_5_words": "streamline data workflows and analysis."
      },
      "text": "\u2022 Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements.\n\u2022 Perform data cleansing, validation, and preprocessing to prepare structured and unstructured data for analysis.\n\u2022 Analyze a variety of sources to check data quality, integrity, and accuracy. Document findings clearly and communicate with the engineers and owners.\n\u2022 Develop and execute queries, scripts, and data manipulation tasks using SQL, Python, or other relevant tools.\n\u2022 Analyze large datasets to identify trends, patterns, and correlations, drawing meaningful conclusions that inform business decisions.\n\u2022 Create clear and concise data visualizations, dashboards, and reports to communicate findings effectively to stakeholders.\n\u2022 Collaborate with clients and cross-functional teams to gather and understand data requirements, translating them into actionable insights.\n\u2022 Work closely with Data Engineers, users, and Business Owners to ensure effective and complete communication about requirements.\n\u2022 Collaborate with Data Scientists and other analysts to support predictive modeling, machine learning, and statistical analysis.\n\u2022 Continuously monitor data quality and proactively identify anomalies or discrepancies, recommending corrective actions.\n\u2022 Stay up-to-date with industry trends, emerging technologies, and best practices to enhance analytical techniques.\n\u2022 Assist in the identification and implementation of process improvements to streamline data workflows and analysis.",
      "word_count": 233
    },
    {
      "bullet_count": 4,
      "heading": "Basic Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 3 + years of proficiency",
        "last_5_words": "Undergraduate or Graduate degree preferred"
      },
      "text": "\u2022 3 + years of proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python].\n\u2022 3+ years of experience supporting Software Engineering, Data Engineering, or Data Analytics projects.\n\u2022 Excellent analytical and problem-solving skills.\n\u2022 Undergraduate or Graduate degree preferred",
      "word_count": 45
    },
    {
      "bullet_count": 10,
      "heading": "Preferred Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Strong proficiency in data",
        "last_5_words": "commitment to accuracy."
      },
      "text": "\u2022 Strong proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python].\n\u2022 Experience with data visualization tools like [Tools - e.g., Tableau, Power BI, Looker].\n\u2022 Experience with cloud data platforms such as [Platforms - e.g., AWS Redshift, Google BigQuery, Azure Synapse].\n\u2022 Familiarity with ETL (Extract, Transform, Load) processes and tools.\n\u2022 Knowledge of machine learning techniques and tools.\n\u2022 Experience in a specific industry (e.g., financial services, healthcare, manufacturing) can be a plus.\n\u2022 Understanding of data governance and data privacy regulations.\n\u2022 Ability to query and manipulate databases and data warehouses.\n\u2022 Strong communication skills with the ability to explain complex data insights to non-technical stakeholders.\n\u2022 Detail-oriented with a commitment to accuracy.",
      "word_count": 118
    }
  ],
  "urls": [
    {
      "type": "website",
      "url": "https://us.nttdata.com"
    },
    {
      "type": "other",
      "url": "https://us.nttdata.com/en/contact-us"
    }
  ]
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
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    },
    {
      "is_primary": true,
      "skill_name": "Excel"
    },
    {
      "is_primary": true,
      "skill_name": "R"
    },
    {
      "is_primary": false,
      "skill_name": "Tableau"
    },
    {
      "is_primary": false,
      "skill_name": "Power BI"
    },
    {
      "is_primary": false,
      "skill_name": "Looker"
    },
    {
      "is_primary": false,
      "skill_name": "AWS Redshift"
    },
    {
      "is_primary": false,
      "skill_name": "Google BigQuery"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Synapse"
    },
    {
      "is_primary": false,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": false,
      "skill_name": "Data Visualization"
    },
    {
      "is_primary": false,
      "skill_name": "Data Analysis"
    },
    {
      "is_primary": false,
      "skill_name": "Data Cleansing"
    },
    {
      "is_primary": false,
      "skill_name": "Data Validation"
    },
    {
      "is_primary": false,
      "skill_name": "Data Preprocessing"
    },
    {
      "is_primary": false,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": false,
      "skill_name": "Data Warehouses"
    },
    {
      "is_primary": false,
      "skill_name": "Databases"
    }
  ],
  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "Senior Data Engineer",
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      "Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
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  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "NTT DATA is a $30 billion",
        "last_5_words": "into the digital future."
      },
      "text": "NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R\u0026D to help organizations and society move confidently and sustainably into the digital future.",
      "word_count": 108
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        "state": "Karn\u0101taka",
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    ],
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        "bullet_count": 12,
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        "source_marker": {
          "first_5_words": "\u2022 Analyze ETL (Extract, transform,",
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        },
        "text": "\u2022 Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements.\n\u2022 Perform data cleansing, validation, and preprocessing to prepare structured and unstructured data for analysis.\n\u2022 Analyze a variety of sources to check data quality, integrity, and accuracy. Document findings clearly and communicate with the engineers and owners.\n\u2022 Develop and execute queries, scripts, and data manipulation tasks using SQL, Python, or other relevant tools.\n\u2022 Analyze large datasets to identify trends, patterns, and correlations, drawing meaningful conclusions that inform business decisions.\n\u2022 Create clear and concise data visualizations, dashboards, and reports to communicate findings effectively to stakeholders.\n\u2022 Collaborate with clients and cross-functional teams to gather and understand data requirements, translating them into actionable insights.\n\u2022 Work closely with Data Engineers, users, and Business Owners to ensure effective and complete communication about requirements.\n\u2022 Collaborate with Data Scientists and other analysts to support predictive modeling, machine learning, and statistical analysis.\n\u2022 Continuously monitor data quality and proactively identify anomalies or discrepancies, recommending corrective actions.\n\u2022 Stay up-to-date with industry trends, emerging technologies, and best practices to enhance analytical techniques.\n\u2022 Assist in the identification and implementation of process improvements to streamline data workflows and analysis.",
        "word_count": 233
      },
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          "last_5_words": "Undergraduate or Graduate degree preferred"
        },
        "text": "\u2022 3 + years of proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python].\n\u2022 3+ years of experience supporting Software Engineering, Data Engineering, or Data Analytics projects.\n\u2022 Excellent analytical and problem-solving skills.\n\u2022 Undergraduate or Graduate degree preferred",
        "word_count": 45
      },
      {
        "bullet_count": 10,
        "heading": "Preferred Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Strong proficiency in data",
          "last_5_words": "commitment to accuracy."
        },
        "text": "\u2022 Strong proficiency in data analysis tools such as [Tools - e.g., Excel, SQL, R, Python].\n\u2022 Experience with data visualization tools like [Tools - e.g., Tableau, Power BI, Looker].\n\u2022 Experience with cloud data platforms such as [Platforms - e.g., AWS Redshift, Google BigQuery, Azure Synapse].\n\u2022 Familiarity with ETL (Extract, Transform, Load) processes and tools.\n\u2022 Knowledge of machine learning techniques and tools.\n\u2022 Experience in a specific industry (e.g., financial services, healthcare, manufacturing) can be a plus.\n\u2022 Understanding of data governance and data privacy regulations.\n\u2022 Ability to query and manipulate databases and data warehouses.\n\u2022 Strong communication skills with the ability to explain complex data insights to non-technical stakeholders.\n\u2022 Detail-oriented with a commitment to accuracy.",
        "word_count": 118
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    ],
    "urls": [
      {
        "type": "website",
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      },
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      }
    ]
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "4af88673-2c35-40e3-a6e5-e6eeb7167844",
  "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",
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      },
      {
        "display_name": "Data Analyst",
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        "role_id": 143,
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    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
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            "kra_text": "Implements data transformation, cleansing, deduplication, and enrichment logic to convert raw source data into analytics-ready curated datasets.",
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            "similarity": 0.7208
          },
          {
            "kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
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            "similarity": 0.6738
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaborate with Data Scientists and other analysts to support predictive modeling, machine learning, and statistical analysis.",
            "similarity": 0.6542
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6829,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
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            "similarity": 0.5764
          },
          {
            "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": 0.5453
          },
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            "similarity": 0.5095
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        ],
        "matched_count": null,
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          },
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          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.5411,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Flutter Developer",
        "kra_matches": [
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          },
          {
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          },
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          }
        ],
        "matched_count": null,
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        "role_id": 74,
        "score": 0.4984,
        "slug": "flutter-developer",
        "total_count": null
      },
      {
        "display_name": "AI Compliance Officer",
        "kra_matches": [
          {
            "kra_text": "Assesses personal data usage, retention schedules, consent mechanisms, and cross-border transfer requirements for AI systems handling sensitive information.",
            "sentence": "Understanding of data governance and data privacy regulations.",
            "similarity": 0.5287
          },
          {
            "kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
            "sentence": "Continuously monitor data quality and proactively identify anomalies or discrepancies, recommending corrective actions.",
            "similarity": 0.4873
          },
          {
            "kra_text": "Coordinates AI incident response procedures, regulatory breach notification, audit investigation support, and remediation tracking for compliance issues.",
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            "similarity": 0.4164
          }
        ],
        "matched_count": null,
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        "role_id": 12,
        "score": 0.4774,
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    ],
    "skill_match_roles": [
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "SQL"
        ],
        "role_id": 121,
        "score": 0.4,
        "slug": "engineering-manager",
        "total_count": 5
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "R"
        ],
        "role_id": 3,
        "score": 0.4,
        "slug": "ml-engineer",
        "total_count": 5
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.4,
        "slug": "data-engineer",
        "total_count": 5
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "R"
        ],
        "role_id": 16,
        "score": 0.4,
        "slug": "ml-ops-engineer",
        "total_count": 5
      },
      {
        "display_name": "AR/VR Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Python"
        ],
        "role_id": 8,
        "score": 0.2,
        "slug": "ar-vr-engineer",
        "total_count": 5
      }
    ]
  },
  "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": 0.93,
      "slug": "data-analyst",
      "total_count": null
    },
    "confidence": 0.93,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Data Analysis",
      "Data Quality Assessment",
      "Data Visualization and Reporting",
      "Stakeholder Communication",
      "Predictive Analytics Support",
      "Data Governance Awareness",
      "Process Improvement"
    ],
    "matched_kras": [
      "Analyze ETL workloads to understand migration requirements",
      "Perform data cleansing, validation, and preprocessing",
      "Check data quality, integrity, and accuracy",
      "Develop and execute queries, scripts, and data manipulation tasks",
      "Analyze large datasets to identify trends, patterns, and correlations",
      "Create data visualizations, dashboards, and reports",
      "Translate data requirements into actionable insights",
      "Monitor data quality and identify anomalies or discrepancies",
      "Assist in the identification and implementation of process improvements"
    ],
    "matched_skills": [
      "ETL",
      "SQL",
      "Python",
      "Excel",
      "R",
      "Tableau",
      "Power BI",
      "Looker",
      "AWS Redshift",
      "Google BigQuery",
      "Azure Synapse",
      "machine learning"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on data analysis, cleansing, validation, visualization, and stakeholder communication rather than building ETL pipelines or data platforms.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 5,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.0,
      "queue_id": 1553,
      "r_and_r_preview": "\u2022 Analyze ETL (Extract, transform, and load) workloads using data from various sources to drive an understanding of migration requirements.\n\u2022 Perform data cleansing, validation, and preprocessing to p",
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      "role_slug": "data-analyst",
      "status": "pending"
    },
    "new_skills_attached": [
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        "is_primary": true,
        "queue_id": 20770,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "ETL",
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      },
      {
        "is_primary": true,
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        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Excel",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20772,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "AWS Redshift",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20773,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Google BigQuery",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20774,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Azure Synapse",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20775,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Visualization",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20776,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Analysis",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20777,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Cleansing",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20778,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Validation",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20779,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Preprocessing",
        "status": "pending"
      },
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        "is_primary": false,
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        "role_slug": "data-analyst",
        "skill_name": "Data Quality",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20781,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Data Warehouses",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 20782,
        "role_display_name": "Data Analyst",
        "role_slug": "data-analyst",
        "skill_name": "Databases",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persisted": false,
      "existing_alias_id": 271,
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      "input_term": "SQL",
      "matched_canonical": {
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        "display_name": "SQL",
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        "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": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "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": 430,
      "existing_alias_text": "R",
      "input_term": "R",
      "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": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 359,
      "existing_alias_text": "Tableau",
      "input_term": "Tableau",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "tableau",
        "sub_category_id": 111,
        "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": 360,
      "existing_alias_text": "Power BI",
      "input_term": "Power BI",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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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": 361,
      "existing_alias_text": "Looker",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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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": 301,
      "existing_alias_text": "Amazon Redshift",
      "input_term": "AWS Redshift",
      "matched_canonical": {
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        "display_name": "Amazon Redshift",
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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",
        "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": 300,
      "existing_alias_text": "BigQuery",
      "input_term": "Google BigQuery",
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      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "existing_alias_id": 302,
      "existing_alias_text": "Azure Synapse Analytics",
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        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
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        "typical_lifespan": "EVERGREEN",
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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": 2015,
      "existing_alias_text": "Machine Learning",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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      "matched_via": "alias"
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      "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.",
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      "slug": "ml-ops-engineer",
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      "slug": "ar-vr-engineer",
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    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on data analysis, cleansing, validation, visualization, and stakeholder communication rather than building ETL pipelines or data platforms.",
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        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
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    },
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        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
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    "Data Quality",
    "Data Warehouses",
    "Databases"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Analyst",
    "id": 143,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on data analysis, cleansing, validation, visualization, and stakeholder communication rather than building ETL pipelines or data platforms.",
    "role_archetype": null,
    "slug": "data-analyst",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Excel",
      "tag": "new"
    },
    {
      "skill": "R",
      "tag": "in_db"
    },
    {
      "skill": "Tableau",
      "tag": "in_db"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "Looker",
      "tag": "in_db"
    },
    {
      "skill": "AWS Redshift",
      "tag": "in_db"
    },
    {
      "skill": "Google BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "Azure Synapse",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Data Visualization",
      "tag": "new"
    },
    {
      "skill": "Data Analysis",
      "tag": "new"
    },
    {
      "skill": "Data Cleansing",
      "tag": "new"
    },
    {
      "skill": "Data Validation",
      "tag": "new"
    },
    {
      "skill": "Data Preprocessing",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "Data Warehouses",
      "tag": "new"
    },
    {
      "skill": "Databases",
      "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": "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": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "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": "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": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "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": "Python",
        "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": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "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": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "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": "Python",
        "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": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "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": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "R",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 194,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "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": "Tableau",
        "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": 150,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "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": "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": "Looker",
        "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": 152,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "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": "AWS Redshift",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 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": "Google BigQuery",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 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": "Azure Synapse",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 143,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "AI Governance and Model Security",
          "id": 50,
          "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
          "slug": "ai-governance-and-model-security",
          "source": "db"
        },
        "dimension_id": 50,
        "input_skill": "Machine Learning",
        "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": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
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