Pipeline run
85b3461f-cb48-43e1-96a6-d33e6551ada2
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 1.00 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Canonical is building a comprehensive automation suite to provide multi-cloud and on-premise data solutions for the enterprise. The data platform team is a collaborative team that develops a managed solutions for a full range of data stores and data technologies, spanning from big data, through NoSQL, cache-layer capabilities, and analytics; all the way to structured SQL engines (similar to Amazon RDS approach). We are facing the interesting problem of fault-tolerant mission-critical distributed systems and intend to deliver the world's best automation solution for delivering managed data platforms. We are looking for candidates from junior to senior level with interests, experience and willingness to learn around Big Data technologies, such as distributed event-stores (Kafka) and parallel computing frameworks (Spark). Engineers who thrive at Canonical are mindful of open-source community dynamics and equally aware of the needs of large, innovative organisations. Location: This is a Globally remote role What your day will look like The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale. This includes ensuring fault-tolerant replication, TLS, installation, backups and much more; but also provides domain-specific expertise on the actual data system to other teams within Canonical. This role is focused on the creation and automation of infrastructure features of data platforms, not analysing and/or processing the data in them. • Collaborate proactively with a distributed team • Write high-quality, idiomatic Python code to create new features • Debug issues and interact with upstream communities publicly • Work with helpful and talented engineers including experts in many fields • Discuss ideas and collaborate on finding good solutions • Work from home with global travel for 2 to 4 weeks per year for internal and external events What we are looking for in you • Proven hands-on experience in software development using Python • Proven hands-on experience in distributed systems, such as Kafka and Spark • Have a Bachelor's or equivalent in Computer Science, STEM, or a similar degree • Willingness to travel up to 4 times a year for internal events Additional Skills That You Might Also Bring You might also bring a subset of experience from the followings that can help Data Platform to achieve its challenging goals and determine the level we will consider you for: • Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise • Experience with Linux systems administration, package management, and infrastructure operations • Experience with the public cloud or a private cloud solution like OpenStack • Experience with operating Kubernetes clusters and a belief that it can be used for serious persistent data services What we offer you Your base pay will depend on various factors including your geographical location, level of experience, knowledge and skills. In addition to the benefits above, certain roles are also eligible for additional benefits and rewards including annual bonuses and sales incentives based on revenue or utilisation. Our compensation philosophy is to ensure equity right across our global workforce. In addition to a competitive base pay, we provide all team members with additional benefits, which reflect our values and ideals. Please note that additional benefits may apply depending on the work location and, for more information on these, please ask your Talent Partner. • Fully remote working environment - we've been working remotely since 2004! • Personal learning and development budget of 2,000USD per annum • Annual compensation review • Recognition rewards • Annual holiday leave • Parental Leave • Employee Assistance Programme • Opportunity to travel to new locations to meet colleagues twice a year • Priority Pass for travel and travel upgrades for long haul company events About Canonical Canonical is a pioneering tech firm that is at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, we are changing the world on a daily basis. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do. Canonical has been a remote-first company since its inception in 2004. Work at Canonical is a step into the future, and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical provides a unique window into the world of 21st-century digital business. Canonical is an equal-opportunity employer We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
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)
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 saved |
|
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) |
Aliases — catalog
- Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Event Stream Store
- Vendor
- Confluent
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 3533
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Asynchronous Messaging and Event Streaming Catalog dimension db id 297
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer
-
Messaging and Background Jobs Catalog dimension db id 291
Library dimension (catalog)
Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer
-
Messaging and Event Streaming Catalog dimension db id 8
Library dimension (catalog)
Roles linked in library: Backend Developer, Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- MySQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- Oracle Corporation
- License
- gpl_v2
- Year introduced
- 1995
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: MySQL appears in a large share of backend/DB job descriptions and remains a standard managed offering across AWS RDS, Cloud SQL, and Azure Database, indicating broad hiring-pipeline demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Data Modeling Catalog dimension db id 216
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Relational Database Usage Catalog dimension db id 371
Library dimension (catalog)
Roles linked in library: Go Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- PostgreSQL (CANONICAL) primary
- PG 13 (VERSION)
- PG 14 (VERSION)
- PG 15 (VERSION)
- PG 16 (VERSION)
- PostgreSQL 13 (VERSION)
- PostgreSQL 14 (VERSION)
- PostgreSQL 15 (VERSION)
- PostgreSQL 16 (VERSION)
- Postgres 13 (VERSION)
- Postgres 14 (VERSION)
- Postgres 15 (VERSION)
- Postgres 16 (VERSION)
- pg10 (VERSION)
- pg11 (VERSION)
- pg12 (VERSION)
- pg13 (VERSION)
- pg14 (VERSION)
- pg15 (VERSION)
- pg16 (VERSION)
- postgres (VERSION)
- postgresql 10 (VERSION)
- postgresql 11 (VERSION)
- postgresql 12 (VERSION)
- postgresql 13 (VERSION)
- postgresql 14 (VERSION)
- postgresql 15 (VERSION)
- postgresql 16 (VERSION)
- postgresql-16 (VERSION)
- postgresql10 (VERSION)
- postgresql11 (VERSION)
- postgresql12 (VERSION)
- postgresql13 (VERSION)
- postgresql14 (VERSION)
- postgresql15 (VERSION)
- postgresql16 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Relational Database
- Vendor
- PostgreSQL Global Development Group
- License
- other_open
- Year introduced
- 1996
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 29
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Data Modeling Catalog dimension db id 216
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Fullstack Developer, PHP Backend Developer
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Relational Database Usage Catalog dimension db id 371
Library dimension (catalog)
Roles linked in library: Go Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- NoSQL (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Database Paradigm
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1019
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
NoSQL Databases Catalog dimension db id 19
Library dimension (catalog)
Roles linked in library: Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- MongoDB (CANONICAL) primary
- MongoDB 2.0 (VERSION)
- MongoDB 2.2 (VERSION)
- MongoDB 2.4 (VERSION)
- MongoDB 2.6 (VERSION)
- MongoDB 3.0 (VERSION)
- MongoDB 3.2 (VERSION)
- MongoDB 3.4 (VERSION)
- MongoDB 3.6 (VERSION)
- MongoDB 4 (VERSION)
- MongoDB 4.0 (VERSION)
- MongoDB 4.2 (VERSION)
- MongoDB 4.4 (VERSION)
- MongoDB 5 (VERSION)
- MongoDB 5.0 (VERSION)
- MongoDB 6 (VERSION)
- MongoDB 6.0 (VERSION)
- MongoDB 7 (VERSION)
- MongoDB 7.0 (VERSION)
- MongoDB 8 (VERSION)
- MongoDB 8.0 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Document Database
- Vendor
- MongoDB, Inc.
- License
- other_open
- Year introduced
- 2009
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 8.0
Maturity reasoning: MongoDB appears in many job descriptions across backend/data roles and is a standard document database in modern stacks; strong GitHub/community activity and broad cloud vendor support indicate mainstream adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 27
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
NoSQL Databases Catalog dimension db id 19
Library dimension (catalog)
Roles linked in library: Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Redis (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Key Value Store
- Vendor
- Redis Labs
- License
- apache_2
- Year introduced
- 2009
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Redis appears in many job descriptions for caching, queues, and session storage, and is a standard datastore in modern backend stacks; vendor activity and broad ecosystem support indicate strong market demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 28
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Caching and State Management Catalog dimension db id 7
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
-
Magento Caching and Performance Catalog dimension db id 404
Library dimension (catalog)
Roles linked in library: Magento Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Caching and State Management
caching-and-state-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Magento Caching and Performance
magento-caching-and-performance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Elasticsearch (CANONICAL) primary
- ES (VERSION)
- ElasticSearch (VERSION)
- Elasticsearch 6 (VERSION)
- Elasticsearch 6.x (VERSION)
- Elasticsearch 7 (VERSION)
- Elasticsearch 7.x (VERSION)
- Elasticsearch 8 (VERSION)
- Elasticsearch 8.x (VERSION)
- Elasticsearch v6 (VERSION)
- Elasticsearch v7 (VERSION)
- Elasticsearch v8 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Search Datastore
- Vendor
- Elastic NV
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.93
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 8.x
Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2925
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Magento Search and Indexing Catalog dimension db id 403
Library dimension (catalog)
Roles linked in library: Magento Dev
-
Search and Content Discovery Catalog dimension db id 356
Library dimension (catalog)
Roles linked in library: Drupal Dev, Sitecore Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Operating Systems
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Kubernetes (CANONICAL) primary
- Kubernetes 1.0+ (VERSION)
- Kubernetes 1.x (VERSION)
- Kubernetes v1 (VERSION)
- k8s (VERSION)
- kubernetes 1.x (VERSION)
- kubernetes latest (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Container Orchestration Platform
- Vendor
- Cloud Native Computing Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.30
Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 557
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Container Orchestration Platforms Catalog dimension db id 134
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Kubernetes for ML Workloads Catalog dimension db id 47
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | 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 |
|---|---|---|---|---|---|---|
| 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 saved | |
| 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) | |
| Kafka | in_db |
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages & 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 | |
| MySQL | in_db |
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MySQL | in_db |
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MySQL | in_db |
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Data Modeling
relational-data-modeling
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Database Design
relational-database-design
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PostgreSQL | in_db |
Relational Database Usage
relational-database-usage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| NoSQL | in_db |
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MongoDB | in_db |
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Redis | in_db |
Caching and State Management
caching-and-state-management
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Redis | in_db |
Magento Caching and Performance
magento-caching-and-performance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Elasticsearch | in_db |
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Elasticsearch | in_db |
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kubernetes | in_db |
Container Orchestration Platforms
container-orchestration-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kubernetes | in_db |
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Linux | type=Operating Systems subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | OpenStack | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Canonical is a pioneering tech",
"last_5_words": "your application fair consideration."
},
"text": "Canonical is a pioneering tech firm that is at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, we are changing the world on a daily basis. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do.\n\nCanonical has been a remote-first company since its inception in 2004. Work at Canonical is a step into the future, and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical provides a unique window into the world of 21st-century digital business.\n\nCanonical is an equal-opportunity employer\n\nWe are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration.",
"word_count": 186
},
"certifications": [],
"company_name": "Canonical",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"SaaS",
"Product Companies"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Have a Bachelor\u0027s or equivalent in Computer Science, STEM, or a similar degree",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
"role": "Data Platform Engineer",
"role_aliases": [
"Data Engineer",
"Big Data Engineer",
"Software Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "What your day will look like",
"heading_was_present": true,
"source_marker": {
"first_5_words": "The data platform team is",
"last_5_words": "for internal and external events"
},
"text": "The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale. This includes ensuring fault-tolerant replication, TLS, installation, backups and much more; but also provides domain-specific expertise on the actual data system to other teams within Canonical. This role is focused on the creation and automation of infrastructure features of data platforms, not analysing and/or processing the data in them.\n\n\u2022 Collaborate proactively with a distributed team\n\u2022 Write high-quality, idiomatic Python code to create new features\n\u2022 Debug issues and interact with upstream communities publicly\n\u2022 Work with helpful and talented engineers including experts in many fields\n\u2022 Discuss ideas and collaborate on finding good solutions\n\u2022 Work from home with global travel for 2 to 4 weeks per year for internal and external events",
"word_count": 164
},
{
"bullet_count": 4,
"heading": "What we are looking for in you",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Proven hands-on experience in",
"last_5_words": "for internal events"
},
"text": "\u2022 Proven hands-on experience in software development using Python\n\u2022 Proven hands-on experience in distributed systems, such as Kafka and Spark\n\u2022 Have a Bachelor\u0027s or equivalent in Computer Science, STEM, or a similar degree\n\u2022 Willingness to travel up to 4 times a year for internal events",
"word_count": 44
},
{
"bullet_count": 4,
"heading": "Additional Skills That You Might Also Bring",
"heading_was_present": true,
"source_marker": {
"first_5_words": "You might also bring a",
"last_5_words": "for serious persistent data services"
},
"text": "You might also bring a subset of experience from the followings that can help Data Platform to achieve its challenging goals and determine the level we will consider you for:\n\n\u2022 Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise\n\u2022 Experience with Linux systems administration, package management, and infrastructure operations\n\u2022 Experience with the public cloud or a private cloud solution like OpenStack\n\u2022 Experience with operating Kubernetes clusters and a belief that it can be used for serious persistent data services",
"word_count": 104
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "Kafka"
},
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": false,
"skill_name": "SQL"
},
{
"is_primary": false,
"skill_name": "MySQL"
},
{
"is_primary": false,
"skill_name": "PostgreSQL"
},
{
"is_primary": false,
"skill_name": "Oracle"
},
{
"is_primary": false,
"skill_name": "NoSQL"
},
{
"is_primary": false,
"skill_name": "MongoDB"
},
{
"is_primary": false,
"skill_name": "Redis"
},
{
"is_primary": false,
"skill_name": "Elasticsearch"
},
{
"is_primary": false,
"skill_name": "Linux"
},
{
"is_primary": false,
"skill_name": "OpenStack"
},
{
"is_primary": false,
"skill_name": "Kubernetes"
}
],
"jd_role": {
"display_name": "Data Platform Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Big Data Engineer",
"Software Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Canonical is a pioneering tech",
"last_5_words": "your application fair consideration."
},
"text": "Canonical is a pioneering tech firm that is at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and the platform for AI, IoT and the cloud, we are changing the world on a daily basis. We recruit on a global basis and set a very high standard for people joining the company. We expect excellence - in order to succeed, we need to be the best at what we do.\n\nCanonical has been a remote-first company since its inception in 2004. Work at Canonical is a step into the future, and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical provides a unique window into the world of 21st-century digital business.\n\nCanonical is an equal-opportunity employer\n\nWe are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration.",
"word_count": 186
},
"certifications": [],
"company_name": "Canonical",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"SaaS",
"Product Companies"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Have a Bachelor\u0027s or equivalent in Computer Science, STEM, or a similar degree",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": null,
"state": null,
"work_mode": "remote"
}
],
"role": "Data Platform Engineer",
"role_aliases": [
"Data Engineer",
"Big Data Engineer",
"Software Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "What your day will look like",
"heading_was_present": true,
"source_marker": {
"first_5_words": "The data platform team is",
"last_5_words": "for internal and external events"
},
"text": "The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale. This includes ensuring fault-tolerant replication, TLS, installation, backups and much more; but also provides domain-specific expertise on the actual data system to other teams within Canonical. This role is focused on the creation and automation of infrastructure features of data platforms, not analysing and/or processing the data in them.\n\n\u2022 Collaborate proactively with a distributed team\n\u2022 Write high-quality, idiomatic Python code to create new features\n\u2022 Debug issues and interact with upstream communities publicly\n\u2022 Work with helpful and talented engineers including experts in many fields\n\u2022 Discuss ideas and collaborate on finding good solutions\n\u2022 Work from home with global travel for 2 to 4 weeks per year for internal and external events",
"word_count": 164
},
{
"bullet_count": 4,
"heading": "What we are looking for in you",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Proven hands-on experience in",
"last_5_words": "for internal events"
},
"text": "\u2022 Proven hands-on experience in software development using Python\n\u2022 Proven hands-on experience in distributed systems, such as Kafka and Spark\n\u2022 Have a Bachelor\u0027s or equivalent in Computer Science, STEM, or a similar degree\n\u2022 Willingness to travel up to 4 times a year for internal events",
"word_count": 44
},
{
"bullet_count": 4,
"heading": "Additional Skills That You Might Also Bring",
"heading_was_present": true,
"source_marker": {
"first_5_words": "You might also bring a",
"last_5_words": "for serious persistent data services"
},
"text": "You might also bring a subset of experience from the followings that can help Data Platform to achieve its challenging goals and determine the level we will consider you for:\n\n\u2022 Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise\n\u2022 Experience with Linux systems administration, package management, and infrastructure operations\n\u2022 Experience with the public cloud or a private cloud solution like OpenStack\n\u2022 Experience with operating Kubernetes clusters and a belief that it can be used for serious persistent data services",
"word_count": 104
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "85b3461f-cb48-43e1-96a6-d33e6551ada2",
"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": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Proven hands-on experience in distributed systems, such as Kafka and Spark",
"similarity": 0.6212
},
{
"kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale.",
"similarity": 0.5867
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "This role is focused on the creation and automation of infrastructure features of data platforms, not analysing and/or processing the data in them.",
"similarity": 0.5077
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5719,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Collaborate proactively with a distributed team",
"similarity": 0.6186
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Discuss ideas and collaborate on finding good solutions",
"similarity": 0.5523
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Work with helpful and talented engineers including experts in many fields",
"similarity": 0.5438
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5716,
"slug": "flutter-developer",
"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": "Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise",
"similarity": 0.5528
},
{
"kra_text": "Debugs full-stack issues that span frontend rendering, API behavior, database queries, and infrastructure configuration to identify root causes.",
"sentence": "Debug issues and interact with upstream communities publicly",
"similarity": 0.5075
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Work with helpful and talented engineers including experts in many fields",
"similarity": 0.4739
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5114,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale.",
"similarity": 0.5153
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "This role is focused on the creation and automation of infrastructure features of data platforms, not analysing and/or processing the data in them.",
"similarity": 0.4728
},
{
"kra_text": "Maintains ML platform runbooks, on-call escalation playbooks, and deployment procedure documentation for production operations teams.",
"sentence": "Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise",
"similarity": 0.4207
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4696,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "frontend debugging and fixes",
"sentence": "Debug issues and interact with upstream communities publicly",
"similarity": 0.5074
},
{
"kra_text": "backend data integration",
"sentence": "The data platform team is responsible for the automation of data platform operations, with the mission of managing and integrating Big Data platforms at scale.",
"similarity": 0.4498
},
{
"kra_text": "backend data integration",
"sentence": "Experience operating and managing other data platform technologies, SQL (MySQL, PostgreSQL, Oracle, etc) and/or NoSQL (MongoDB, Redis, ElasticSearch, etc), similar to DBA level expertise",
"similarity": 0.4403
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.4658,
"slug": "svelte-frontend-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Apache Spark",
"Kafka",
"Python"
],
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": 3
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Kafka",
"Python"
],
"role_id": 1,
"score": 0.6667,
"slug": "backend-engineer",
"total_count": 3
},
{
"display_name": "Python Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Kafka",
"Python"
],
"role_id": 80,
"score": 0.6667,
"slug": "python-backend-developer",
"total_count": 3
},
{
"display_name": "AR/VR Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Python"
],
"role_id": 8,
"score": 0.3333,
"slug": "ar-vr-engineer",
"total_count": 3
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Python"
],
"role_id": 5,
"score": 0.3333,
"slug": "cybersecurity-engineer",
"total_count": 3
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 1.00 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 476,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": false,
"queue_id": 22441,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Oracle",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 22442,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Linux",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 22443,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "OpenStack",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
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"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "PostgreSQL",
"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": "Oracle",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "oracle",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "NoSQL",
"alias_type": "CANONICAL",
"id": 1989,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "NoSQL",
"id": 1346,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "nosql",
"sub_category_id": 1019,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "NoSQL Databases",
"id": 19,
"rationale": "Models and manages data using non-relational database systems.",
"slug": "nosql-databases",
"source": "db"
},
"input_skill": "NoSQL",
"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"
}
]
}
],
"input_skill": "NoSQL",
"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": "MongoDB",
"alias_type": "CANONICAL",
"id": 232,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 2.0",
"alias_type": "VERSION",
"id": 238,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 2.2",
"alias_type": "VERSION",
"id": 239,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 2.4",
"alias_type": "VERSION",
"id": 240,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 2.6",
"alias_type": "VERSION",
"id": 241,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 3.0",
"alias_type": "VERSION",
"id": 242,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 3.2",
"alias_type": "VERSION",
"id": 243,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 3.4",
"alias_type": "VERSION",
"id": 244,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 3.6",
"alias_type": "VERSION",
"id": 245,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4",
"alias_type": "VERSION",
"id": 233,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4.0",
"alias_type": "VERSION",
"id": 246,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4.2",
"alias_type": "VERSION",
"id": 247,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4.4",
"alias_type": "VERSION",
"id": 248,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 5",
"alias_type": "VERSION",
"id": 234,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 5.0",
"alias_type": "VERSION",
"id": 249,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 6",
"alias_type": "VERSION",
"id": 235,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 6.0",
"alias_type": "VERSION",
"id": 250,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 7",
"alias_type": "VERSION",
"id": 236,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 7.0",
"alias_type": "VERSION",
"id": 251,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 8",
"alias_type": "VERSION",
"id": 237,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 8.0",
"alias_type": "VERSION",
"id": 252,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "MongoDB",
"id": 91,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "mongodb",
"sub_category_id": 27,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "NoSQL Databases",
"id": 19,
"rationale": "Models and manages data using non-relational database systems.",
"slug": "nosql-databases",
"source": "db"
},
"input_skill": "MongoDB",
"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"
}
]
}
],
"input_skill": "MongoDB",
"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": "Redis",
"alias_type": "CANONICAL",
"id": 168,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Redis",
"id": 31,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "redis",
"sub_category_id": 28,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Caching and State Management",
"id": 7,
"rationale": "Techniques and systems for reducing latency and managing ephemeral backend state. Covers cache-aside patterns, distributed caches, session stores, and invalidation strategies.",
"slug": "caching-and-state-management",
"source": "db"
},
"input_skill": "Redis",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Magento Caching and Performance",
"id": 404,
"rationale": "Performance tuning specific to Magento storefronts and their rendering pipeline. This includes cache layers, page generation costs, and practical optimizations that preserve upgradeability while improving shopper experience.",
"slug": "magento-caching-and-performance",
"source": "db"
},
"input_skill": "Redis",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Magento Dev",
"id": 231,
"rationale": null,
"role_archetype": "Engineering",
"slug": "magento-dev",
"source": "db"
}
]
}
],
"input_skill": "Redis",
"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": "Elasticsearch",
"alias_type": "CANONICAL",
"id": 4648,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ES",
"alias_type": "VERSION",
"id": 4649,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ElasticSearch",
"alias_type": "VERSION",
"id": 4650,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6",
"alias_type": "VERSION",
"id": 4651,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6.x",
"alias_type": "VERSION",
"id": 4657,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7",
"alias_type": "VERSION",
"id": 4652,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7.x",
"alias_type": "VERSION",
"id": 4658,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8",
"alias_type": "VERSION",
"id": 4653,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8.x",
"alias_type": "VERSION",
"id": 4659,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v6",
"alias_type": "VERSION",
"id": 4654,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v7",
"alias_type": "VERSION",
"id": 4655,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v8",
"alias_type": "VERSION",
"id": 4656,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Elasticsearch",
"id": 3171,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "elasticsearch",
"sub_category_id": 2925,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Magento Search and Indexing",
"id": 403,
"rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
"slug": "magento-search-and-indexing",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Magento Dev",
"id": 231,
"rationale": null,
"role_archetype": "Engineering",
"slug": "magento-dev",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Search and Content Discovery",
"id": 356,
"rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
"slug": "search-and-content-discovery",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"source": "db"
},
{
"display_name": "Sitecore Dev",
"id": 233,
"rationale": null,
"role_archetype": "Engineering",
"slug": "sitecore-dev",
"source": "db"
}
]
}
],
"input_skill": "Elasticsearch",
"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": "Linux",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Operating Systems",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "linux",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "OpenStack",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"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": "openstack",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Kubernetes",
"alias_type": "CANONICAL",
"id": 1267,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0+",
"alias_type": "VERSION",
"id": 1271,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.x",
"alias_type": "VERSION",
"id": 1270,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes v1",
"alias_type": "VERSION",
"id": 1269,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "k8s",
"alias_type": "VERSION",
"id": 1268,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "kubernetes 1.x",
"alias_type": "VERSION",
"id": 1400,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "kubernetes latest",
"alias_type": "VERSION",
"id": 1401,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Kubernetes",
"id": 726,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "kubernetes",
"sub_category_id": 557,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Container Orchestration Platforms",
"id": 134,
"rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
"slug": "container-orchestration-platforms",
"source": "db"
},
"input_skill": "Kubernetes",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Kubernetes for ML Workloads",
"id": 47,
"rationale": "Kubernetes-native components used to schedule, accelerate, and isolate ML training and serving workloads. This includes GPU enablement and ML-specific controllers rather than generic cluster administration.",
"slug": "kubernetes-for-ml-workloads",
"source": "db"
},
"input_skill": "Kubernetes",
"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": "Kubernetes",
"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": [
"Oracle",
"Linux",
"OpenStack"
]
}
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 1.00 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Kafka",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "MySQL",
"tag": "in_db"
},
{
"skill": "PostgreSQL",
"tag": "in_db"
},
{
"skill": "Oracle",
"tag": "new"
},
{
"skill": "NoSQL",
"tag": "in_db"
},
{
"skill": "MongoDB",
"tag": "in_db"
},
{
"skill": "Redis",
"tag": "in_db"
},
{
"skill": "Elasticsearch",
"tag": "in_db"
},
{
"skill": "Linux",
"tag": "new"
},
{
"skill": "OpenStack",
"tag": "new"
},
{
"skill": "Kubernetes",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud 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,
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