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Pipeline run

383780b5-cb75-46b1-89e9-76361f25f36a

Pipeline LLM cost (USD)
API 1: $0.0041 API 2: $0.0002 API 3: $0.0000 Total: $0.0043

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and operate scalable data pipelines and API integrations, model and transform data with SQL/ETL-ELT, and publish operational/analytical datasets and reports across Finance, Sales, Cloud, and telemetry sources.
"Develops and maintains scalable data pipelines and builds out new API integrations."
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted cloud and analytics tools like AWS, RDS, S3, SQL, Tableau, and Metabase, which is characteristic of a mainstream modern data engineering environment.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (16)
SQL ETL ELT Data Modeling Data Warehousing API AWS S3 RDS Tableau Metabase SingleStore Prometheus PromQL Grafana Go
Skill cluster (6 dimension groups, role-scoped)
Cloud Platforms
AWS S3 RDS
BI and Visualization Tools
Tableau Metabase
Observability and Operations
Prometheus Grafana
Go Language and Toolchain
Go
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
ETL ELT Data Modeling Data Warehousing API SingleStore PromQL
Show KRA description ↓
Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers. 3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor's degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment Experience with SingleStore as application developer or engineerStrong SQL skills with experience in writing complex SQLs, materialized views, high performance queries.Experience with AWS services such as S3 and RDS.Experience with Tableau, Metabase, and other data visualization toolsStrong knowledge of data management fundamentals and data storage principles.Experience in working and delivering end-to-end projects independently.Experience with Prometheus, PromQL, Grafana, and the Go programming language is a PlusStrong written and verbal communication skills across diverse audiences.

Signals

Skill data-engineer
0.50
Alias data-engineer
1.00
KRA data-engineer
0.59

Post-classification

Centroidupdated · n=403
Alias collision log
New-role queue
New skills captured6
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
ELT primary Data Engineer pending
Data Modeling primary Data Engineer pending
Data Warehousing primary Data Engineer pending
SingleStore primary Data Engineer pending
PromQL Data Engineer pending
Status: completed Created: 2026-05-27T16:08:53.041598Z Updated: 2026-05-27T16:11:00.246427Z API 3 duration: 72577 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.50 does not contradict

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

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

Job description

Description

SingleStore is a cutting edge business leading a wave of disruption in the database space focussed on delivering a single platform for all data intensive applications.

Job Responsibilities
Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.
Basic Qualifications
3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor's degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment
Preferred Qualifications
Experience with SingleStore as application developer or engineerStrong SQL skills with experience in writing complex SQLs, materialized views, high performance queries.Experience with AWS services such as S3 and RDS.Experience with Tableau, Metabase, and other data visualization toolsStrong knowledge of data management fundamentals and data storage principles.Experience in working and delivering end-to-end projects independently.Experience with Prometheus, PromQL, Grafana, and the Go programming language is a PlusStrong written and verbal communication skills across diverse audiences.
Benefits
Company WideTechnology Stipend for New Employees Monthly Cell Phone and Internet StipendHealth and Wellness benefit Company and team events Flexible time off Volunteer time offStock Options 
As employees are located in many different countries around the world, some benefits may differ from country to country. In all cases, we do our best to provide equitable perks and benefits across our locations.

Other
Full Time Employment Eligibility to work for an India based employerFully Remote Role or Hybrid based in India - Hyderabad or Bangalore.
SingleStore is one platform for all data, built so you can engage with insight in every moment. Trusted by industry leaders, SingleStore enables enterprises to adapt to change as it happens, embrace diverse data with ease, and accelerate the pace of innovation. SingleStore is venture-backed and headquartered in San Francisco with offices in Sunnyvale, Seattle, Boston, London, Lisbon, Bangalore, Dublin and Kyiv. Defining the future starts with The Single Database for All Data-Intensive Applications.

Consistent with our commitment to diversity & inclusion, we value individuals with the ability to work on diverse teams and with a diverse range of people.

Please note that SingleStore’s COVID-19 vaccination policy requires that team members in the United States be fully vaccinated, including a booster shot with one of the United States FDA-approved vaccine options to meet in person for SingleStore business or to work from one of our U.S. office locations.[It is expected that this will be a requirement for this role]. If an exemption and/or accommodation to our vaccination policy is requested, a member of the Human Resources department will be available to begin the interactive accommodation process.

To all recruitment agencies: SingleStore does not accept agency resumes. Please do not forward resumes to SingleStore employees. SingleStore is not responsible for any fees related to unsolicited resumes and will not pay fees to any third-party agency or company that does not have a signed agreement with the Company.

Skills from this JD

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

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

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

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

API 3 link attempts (this skill)

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
API Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: API id=1568 · api

Aliases — catalog

  • API (CANONICAL)

Context tags (catalog)

API gateway GraphQL JSON OAuth REST SDK SOAP XML authentication endpoint microservices rate limiting throttling versioning webhooks

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Application Programming Interface
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: APIs are a core requirement in most software engineering JDs and underpin common integrations across cloud, mobile, and web stacks; major vendors like AWS, Stripe, and Google Cloud center products on API-first usage.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1174
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Integration and Data Fetching Catalog dimension db id 127

    Library dimension (catalog)

    Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

API Gateway AWS CLI Auto Scaling CloudFormation CloudFront CloudTrail CloudWatch Cognito DynamoDB EC2 ECS EKS Elastic Beanstalk Elastic Load Balancing IAM KMS Lambda RDS Route 53 S3 SNS SQS Serverless VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Amazon
License
other_open
Year introduced
2006
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: S3 id=1212 · s3

Aliases — catalog

  • S3 (CANONICAL)

Context tags (catalog)

AWS AWS CLI CORS CloudFormation IAM policies S3 Select bucket data lifecycle encryption event notifications multipart upload object storage static website hosting transfer acceleration versioning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Storage Platform
Vendor
Amazon
License
proprietary
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a default cloud storage service in AWS job descriptions and architecture docs; it remains broadly adopted with no vendor sunset, and is commonly paired with S3-compatible storage rather than replaced.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
919
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RDS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RDS id=1211 · rds

Aliases — catalog

  • RDS (CANONICAL)

Context tags (catalog)

AWS Aurora MySQL PostgreSQL SQL Server backup and restore cloudformation database migration high availability multi-AZ parameter groups performance tuning read replicas scalability security groups

Stored enrichment (catalog DB)

Category
Service
Sub-category
Managed Relational Database Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2009
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS RDS is a standard managed database service and appears frequently in cloud/DevOps job descriptions alongside PostgreSQL/MySQL on AWS, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
918
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Tableau Primary 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 saved
Metabase Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Metabase id=153 · metabase

Aliases — catalog

  • Metabase (CANONICAL) primary

Context tags (catalog)

BigQuery MySQL PostgreSQL Redshift SQL ad hoc analysis cards collections dashboards data visualization embedded analytics filters questions segments self-service BI

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Bi Analytics Tool
Vendor
Metabase, Inc.
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Metabase appears in many BI/analytics job postings and is growing in GitHub usage, but it is still far less universal than Tableau/Power BI in enterprise JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
125
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 saved
SingleStore Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Prometheus Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Prometheus id=46 · prometheus

Aliases — catalog

  • Prometheus (CANONICAL) primary

Context tags (catalog)

Alertmanager Grafana Kubernetes OpenMetrics PromQL Pushgateway TSDB Thanos alert rules alerts cAdvisor cloud-native container orchestration dashboards exporter kube-state-metrics labels metrics metrics endpoint metrics scraping monitoring node_exporter observability open-source pushgateway recording rules remote write scrape interval scraping service discovery time series visualization

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Monitoring Platform
Vendor
Cloud Native Computing Foundation
License
apache_2
Year introduced
2012
Confidence
0.62
Version strategy
NOT_APPLICABLE

Maturity reasoning: Prometheus is widely listed in DevOps/SRE job descriptions and is a standard CNCF monitoring stack component, often paired with Grafana for production observability.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
2837
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PromQL 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
Monitoring Tools
Sub-category
general
Skill nature
LANGUAGE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Grafana Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Grafana id=47 · grafana

Aliases — catalog

  • Grafana (CANONICAL) primary

Context tags (catalog)

Alertmanager Grafana API Grafana Cloud Grafana Enterprise Grafana Loki Grafana Tempo Grafana plugins InfluxDB Kubernetes Loki Prometheus SLA SLO SRE Tempo alerting alerts annotations dashboard dashboarding dashboards data source data sources data visualization influxDB metrics monitoring observability plugins query editor templating time series time-series visualization

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Observability Platform
Vendor
Grafana Labs
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Grafana appears in many DevOps/SRE job descriptions and is a standard observability dashboarding tool alongside Prometheus and Loki; strong GitHub/community adoption and broad vendor integrations signal mainstream use.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
176
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Incident Triage Catalog dimension db id 155

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Go id=3 · go

Aliases — catalog

  • Go (CANONICAL) primary
  • Go 1.0 (VERSION)
  • Go 1.1 (VERSION)
  • Go 1.10 (VERSION)
  • Go 1.11 (VERSION)
  • Go 1.12 (VERSION)
  • Go 1.13 (VERSION)
  • Go 1.14 (VERSION)
  • Go 1.15 (VERSION)
  • Go 1.16 (VERSION)
  • Go 1.17 (VERSION)
  • Go 1.18 (VERSION)
  • Go 1.19 (VERSION)
  • Go 1.2 (VERSION)
  • Go 1.20 (VERSION)
  • Go 1.21 (VERSION)
  • Go 1.22 (VERSION)
  • Go 1.3 (VERSION)
  • Go 1.4 (VERSION)
  • Go 1.5 (VERSION)
  • Go 1.6 (VERSION)
  • Go 1.7 (VERSION)
  • Go 1.8 (VERSION)
  • Go 1.9 (VERSION)
  • Go 1.x (VERSION)
  • Golang (VERSION)
  • go 1.18 (VERSION)
  • go 1.19 (VERSION)
  • go 1.20 (VERSION)
  • go 1.21 (VERSION)
  • go 1.22 (VERSION)
  • go latest (VERSION)
  • go1.18 (VERSION)
  • go1.19 (VERSION)
  • go1.20 (VERSION)
  • go1.21 (VERSION)
  • go1.22 (VERSION)
  • go1.x (VERSION)
  • golang (VERSION)

Context tags (catalog)

Beego Channel Concurrency Context Docker Echo Gin Go modules Golang Goroutine HTTP HTTP server Interface JSON JSON encoding Kubernetes Microservices REST API Struct Testing channels concurrency context package context.Context error handling gRPC goroutines interfaces microservices middleware modules net/http protobuf structs testing

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Google
License
bsd
Year introduced
2009
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Go appears in many current job descriptions for backend, cloud, and DevOps roles, and is a standard language in Kubernetes, Docker, and cloud-native stacks; strong GitHub and ecosystem adoption signal broad market demand.

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

  • Go Language and Toolchain Catalog dimension db id 367

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • 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
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go Language and Toolchain
go-language-and-toolchain
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 and Scripting
programming-languages-and-scripting
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)

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 saved
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
API in_db
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
S3 in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 in_db
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
RDS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Metabase in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Prometheus in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prometheus in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Incident Triage
observability-and-incident-triage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Grafana in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Go Language and Toolchain
go-language-and-toolchain
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Go in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed SingleStore | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed PromQL | type=Monitoring Tools subtype=general nature=LANGUAGE lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
CompanySingleStore
Experience3+ years of experience as a Data Engineer or in a similar role
DomainSoftware & SaaS Products
Location Hyderabad, India (hybrid)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "SingleStore is a cutting edge",
      "last_5_words": "for All Data-Intensive Applications."
    },
    "text": "SingleStore is a cutting edge business leading a wave of disruption in the database space focussed on delivering a single platform for all data intensive applications. SingleStore is venture-backed and headquartered in San Francisco with offices in Sunnyvale, Seattle, Boston, London, Lisbon, Bangalore, Dublin and Kyiv. Defining the future starts with The Single Database for All Data-Intensive Applications.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "SingleStore",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "SaaS",
        "Data Platforms"
      ],
      "domain": "Software \u0026 SaaS Products"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE/BSC - Computer Science / Engineering / Mathematics (or related)",
      "raw": "Bachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 3,
    "raw": "3+ years of experience as a Data Engineer or in a similar role"
  },
  "job_locations": [
    {
      "aliases": [
        "Hyderabad, TG"
      ],
      "city": "Hyderabad",
      "country": "India",
      "state": "Telangana",
      "work_mode": "hybrid"
    },
    {
      "aliases": [
        "Bengaluru",
        "Bangalore, KA"
      ],
      "city": "Bangalore",
      "country": "India",
      "state": "Karnataka",
      "work_mode": "hybrid"
    }
  ],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Developer",
    "ETL Developer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Job Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Translate business and functional requirements",
        "last_5_words": "and mentor junior data engineers."
      },
      "text": "Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.",
      "word_count": 206
    },
    {
      "bullet_count": 0,
      "heading": "Basic Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "3+ years of experience as a",
        "last_5_words": "intellectually curious environment."
      },
      "text": "3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment",
      "word_count": 83
    },
    {
      "bullet_count": 0,
      "heading": "Preferred Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Experience with SingleStore as application",
        "last_5_words": "across diverse audiences."
      },
      "text": "Experience with SingleStore as application developer or engineerStrong SQL skills with experience in writing complex SQLs, materialized views, high performance queries.Experience with AWS services such as S3 and RDS.Experience with Tableau, Metabase, and other data visualization toolsStrong knowledge of data management fundamentals and data storage principles.Experience in working and delivering end-to-end projects independently.Experience with Prometheus, PromQL, Grafana, and the Go programming language is a PlusStrong written and verbal communication skills across diverse audiences.",
      "word_count": 92
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    },
    {
      "is_primary": true,
      "skill_name": "API"
    },
    {
      "is_primary": true,
      "skill_name": "AWS"
    },
    {
      "is_primary": true,
      "skill_name": "S3"
    },
    {
      "is_primary": true,
      "skill_name": "RDS"
    },
    {
      "is_primary": true,
      "skill_name": "Tableau"
    },
    {
      "is_primary": true,
      "skill_name": "Metabase"
    },
    {
      "is_primary": true,
      "skill_name": "SingleStore"
    },
    {
      "is_primary": false,
      "skill_name": "Prometheus"
    },
    {
      "is_primary": false,
      "skill_name": "PromQL"
    },
    {
      "is_primary": false,
      "skill_name": "Grafana"
    },
    {
      "is_primary": false,
      "skill_name": "Go"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "SingleStore is a cutting edge",
        "last_5_words": "for All Data-Intensive Applications."
      },
      "text": "SingleStore is a cutting edge business leading a wave of disruption in the database space focussed on delivering a single platform for all data intensive applications. SingleStore is venture-backed and headquartered in San Francisco with offices in Sunnyvale, Seattle, Boston, London, Lisbon, Bangalore, Dublin and Kyiv. Defining the future starts with The Single Database for All Data-Intensive Applications.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "SingleStore",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "SaaS",
          "Data Platforms"
        ],
        "domain": "Software \u0026 SaaS Products"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE/BSC - Computer Science / Engineering / Mathematics (or related)",
        "raw": "Bachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 3,
      "raw": "3+ years of experience as a Data Engineer or in a similar role"
    },
    "job_locations": [
      {
        "aliases": [
          "Hyderabad, TG"
        ],
        "city": "Hyderabad",
        "country": "India",
        "state": "Telangana",
        "work_mode": "hybrid"
      },
      {
        "aliases": [
          "Bengaluru",
          "Bangalore, KA"
        ],
        "city": "Bangalore",
        "country": "India",
        "state": "Karnataka",
        "work_mode": "hybrid"
      }
    ],
    "role": "Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Job Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Translate business and functional requirements",
          "last_5_words": "and mentor junior data engineers."
        },
        "text": "Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.",
        "word_count": 206
      },
      {
        "bullet_count": 0,
        "heading": "Basic Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "3+ years of experience as a",
          "last_5_words": "intellectually curious environment."
        },
        "text": "3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment",
        "word_count": 83
      },
      {
        "bullet_count": 0,
        "heading": "Preferred Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Experience with SingleStore as application",
          "last_5_words": "across diverse audiences."
        },
        "text": "Experience with SingleStore as application developer or engineerStrong SQL skills with experience in writing complex SQLs, materialized views, high performance queries.Experience with AWS services such as S3 and RDS.Experience with Tableau, Metabase, and other data visualization toolsStrong knowledge of data management fundamentals and data storage principles.Experience in working and delivering end-to-end projects independently.Experience with Prometheus, PromQL, Grafana, and the Go programming language is a PlusStrong written and verbal communication skills across diverse audiences.",
        "word_count": 92
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "383780b5-cb75-46b1-89e9-76361f25f36a",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.",
            "similarity": 0.6475
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment",
            "similarity": 0.5351
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5913,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.",
            "similarity": 0.5294
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "3+ years of experience as a Data Engineer or in a similar roleExperience with data modeling, data warehousing, and building ETL pipelinesExperience in SQLBachelor\u0027s degree in computer science, engineering, mathematics, or a related technical discipline4+ years of industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasetsKnowledge of data management fundamentals and data storage principlesA desire to work in a collaborative, intellectually curious environment",
            "similarity": 0.4031
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4662,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Translate business and functional requirements into robust, scalable, operable solutions that work well within the overall data architecture.Design, develop, implement, test, document, and operate large-scale, high-volume and low latency applications.Build integrations between multiple data sources from every corner of our business including Finance, Sales, Cloud, Product Telemetry, and others.Designs data integrations and data quality framework.Develops and maintains scalable data pipelines and builds out new API integrations.Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL.Manage stakeholder communication, prioritization of tasks and on time solution delivery.Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance.Produce comprehensive, usable dataset documentation and metadata.Design and develop operational and analytical reports as per the customer needs by using the tools.Evaluate and make decisions around the use of new or existing software products and tools.Mentor junior data engineers.",
            "similarity": 0.5347
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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    "matched_dimensions": [],
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    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.50 does not contradict",
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    "centroid_n_after": 403,
    "centroid_updated": true,
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    "new_kra_attached": null,
    "new_skills_attached": [
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        "skill_name": "ETL",
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        "is_primary": true,
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        "role_display_name": "Data Engineer",
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        "skill_name": "ELT",
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}
API 2 — extract-details
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        "is_extractable": true,
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
      "input_term": "Data Modeling",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "embedding_alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2514,
      "existing_alias_text": "API",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "matched_via": "alias"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Tableau",
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      "matched_via": "alias"
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        "skill_nature": "TOOL",
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      "matched_via": "alias"
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        "source": "db"
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        "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Backend Observability, Logging, and Diagnostics",
            "id": 388,
            "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
            "slug": "backend-observability-logging-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Prometheus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Response",
            "id": 10,
            "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
            "slug": "observability-and-incident-response",
            "source": "db"
          },
          "input_skill": "Prometheus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Triage",
            "id": 155,
            "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
            "slug": "observability-and-incident-triage",
            "source": "db"
          },
          "input_skill": "Prometheus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Operations",
            "id": 143,
            "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
            "slug": "observability-and-operations",
            "source": "db"
          },
          "input_skill": "Prometheus",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Prometheus",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "PromQL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Monitoring Tools",
          "skill_nature": "LANGUAGE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "promql",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Grafana",
          "alias_type": "CANONICAL",
          "id": 184,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Grafana",
        "id": 47,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "grafana",
        "sub_category_id": 176,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Backend Observability, Logging, and Diagnostics",
            "id": 388,
            "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
            "slug": "backend-observability-logging-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Grafana",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Response",
            "id": 10,
            "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
            "slug": "observability-and-incident-response",
            "source": "db"
          },
          "input_skill": "Grafana",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Triage",
            "id": 155,
            "rationale": "Telemetry, alerting, and troubleshooting practices used to diagnose failed builds, broken deployments, and unhealthy release environments. This is a coherent cluster because delivery reliability depends on quickly identifying where the workflow failed.",
            "slug": "observability-and-incident-triage",
            "source": "db"
          },
          "input_skill": "Grafana",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Operations",
            "id": 143,
            "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
            "slug": "observability-and-operations",
            "source": "db"
          },
          "input_skill": "Grafana",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Grafana",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Go",
          "alias_type": "CANONICAL",
          "id": 25,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.0",
          "alias_type": "VERSION",
          "id": 27,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.1",
          "alias_type": "VERSION",
          "id": 28,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.10",
          "alias_type": "VERSION",
          "id": 39,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.11",
          "alias_type": "VERSION",
          "id": 40,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.12",
          "alias_type": "VERSION",
          "id": 41,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.13",
          "alias_type": "VERSION",
          "id": 42,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.14",
          "alias_type": "VERSION",
          "id": 43,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.15",
          "alias_type": "VERSION",
          "id": 44,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.16",
          "alias_type": "VERSION",
          "id": 45,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.17",
          "alias_type": "VERSION",
          "id": 46,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.18",
          "alias_type": "VERSION",
          "id": 47,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.19",
          "alias_type": "VERSION",
          "id": 48,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.2",
          "alias_type": "VERSION",
          "id": 29,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.20",
          "alias_type": "VERSION",
          "id": 49,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.21",
          "alias_type": "VERSION",
          "id": 50,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.22",
          "alias_type": "VERSION",
          "id": 51,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.3",
          "alias_type": "VERSION",
          "id": 30,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.4",
          "alias_type": "VERSION",
          "id": 31,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.5",
          "alias_type": "VERSION",
          "id": 32,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.6",
          "alias_type": "VERSION",
          "id": 33,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.7",
          "alias_type": "VERSION",
          "id": 34,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.8",
          "alias_type": "VERSION",
          "id": 35,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.9",
          "alias_type": "VERSION",
          "id": 36,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Go 1.x",
          "alias_type": "VERSION",
          "id": 37,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Golang",
          "alias_type": "VERSION",
          "id": 38,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go 1.18",
          "alias_type": "VERSION",
          "id": 2858,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go 1.19",
          "alias_type": "VERSION",
          "id": 2859,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go 1.20",
          "alias_type": "VERSION",
          "id": 2860,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go 1.21",
          "alias_type": "VERSION",
          "id": 2861,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go 1.22",
          "alias_type": "VERSION",
          "id": 2407,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go latest",
          "alias_type": "VERSION",
          "id": 2408,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.18",
          "alias_type": "VERSION",
          "id": 2852,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.19",
          "alias_type": "VERSION",
          "id": 2853,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.20",
          "alias_type": "VERSION",
          "id": 2854,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.21",
          "alias_type": "VERSION",
          "id": 2855,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.22",
          "alias_type": "VERSION",
          "id": 2405,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "go1.x",
          "alias_type": "VERSION",
          "id": 2404,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "golang",
          "alias_type": "VERSION",
          "id": 2406,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Go",
        "id": 3,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "go",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Go",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Go Language and Toolchain",
            "id": 367,
            "rationale": "Core Go implementation skills used to build backend service logic, handlers, and internal libraries. This is the primary coding surface for the role and includes language features, module management, and compiler/runtime behavior relevant to service code.",
            "slug": "go-language-and-toolchain",
            "source": "db"
          },
          "input_skill": "Go",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Go",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Go",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Go",
          "llm_role": null,
          "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"
            }
          ]
        }
      ],
      "input_skill": "Go",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "ELT",
    "Data Warehousing",
    "SingleStore",
    "PromQL"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.50 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
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