Pipeline run
26a5dd1d-ddb6-49ce-b8db-c77c2abd8359
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
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Title: Data Engineer (Java Backend) Location: Bangalore Experience: 3 to 6 years Employment Type: Full Time Job Description: We are seeking a talented and experienced Data Engineer proficient in Java to join our team in Bangalore. As a Data Engineer, you will be responsible for designing, developing, and maintaining data pipelines, ETL processes, and data infrastructure to support our organisation's data needs. The ideal candidate should have a strong background in Java development with a focus on data engineering, along with excellent problem-solving skills and a passion for working with large datasets. Responsibilities: 1. Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies. 2. Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions. 3. Optimise and fine-tune existing data pipelines for improved performance and reliability. 4. Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems. 5. Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies. 6. Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations. 7. Stay up-to-date with emerging technologies and trends in data engineering and incorporate them into our data infrastructure and processes. 8. Document design specifications, technical solutions, and best practices for knowledge sharing and future reference. Requirements: 1. Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 2. 3 to 6 years of professional experience in software development with a focus on data engineering. 3. Strong proficiency in Java programming language, with hands-on experience in building scalable applications and data pipelines. 4. Experience with distributed computing frameworks such as Apache Hadoop, Spark, or Flink. 5. Proficiency in SQL and experience with relational databases like MySQL, PostgreSQL, or Oracle. 6. Experience with NoSQL databases such as MongoDB, Cassandra, or Redis is a plus. 7. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, and experience with related services like AWS Glue, Azure Data Factory, or Google BigQuery. 8. Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and implement effective solutions. 9. Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment. 10. Proven ability to manage multiple priorities and deliver results in a fast-paced and dynamic environment. Preferred Qualifications: 1. Experience with data visualisation tools such as Tableau, Power BI, or Looker. 2. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes. 3. Knowledge of stream processing frameworks such as Apache Kafka or Apache Storm. 4. Experience with agile software development methodologies like Scrum or Kanban. Join us in building innovative data solutions that drive business growth and unlock insights from large-scale data sets. If you have a passion for data engineering and thrive in a collaborative environment, we'd love to hear from you!
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
- Java (CANONICAL) primary
- JDK (VERSION)
- JDK 10 (VERSION)
- JDK 11 (VERSION)
- JDK 12 (VERSION)
- JDK 13 (VERSION)
- JDK 14 (VERSION)
- JDK 15 (VERSION)
- JDK 16 (VERSION)
- JDK 17 (VERSION)
- JDK 18 (VERSION)
- JDK 19 (VERSION)
- JDK 20 (VERSION)
- JDK 21 (VERSION)
- JDK 5 (VERSION)
- JDK 6 (VERSION)
- JDK 7 (VERSION)
- JDK 8 (VERSION)
- JDK 9 (VERSION)
- Java 1.0 (VERSION)
- Java 1.1 (VERSION)
- Java 1.2 (VERSION)
- Java 1.3 (VERSION)
- Java 1.4 (VERSION)
- Java 1.5 (VERSION)
- Java 1.6 (VERSION)
- Java 1.7 (VERSION)
- Java 1.8 (VERSION)
- Java 10 (VERSION)
- Java 11 (VERSION)
- Java 12 (VERSION)
- Java 13 (VERSION)
- Java 14 (VERSION)
- Java 15 (VERSION)
- Java 16 (VERSION)
- Java 17 (VERSION)
- Java 18 (VERSION)
- Java 19 (VERSION)
- Java 20 (VERSION)
- Java 21 (VERSION)
- Java 5 (VERSION)
- Java 6 (VERSION)
- Java 7 (VERSION)
- Java 8 (VERSION)
- Java 9 (VERSION)
- Java11 (VERSION)
- Java17 (VERSION)
- Java21 (VERSION)
- Java8 (VERSION)
- OpenJDK 11 (VERSION)
- OpenJDK 17 (VERSION)
- OpenJDK 21 (VERSION)
- OpenJDK 8 (VERSION)
- java 11 (VERSION)
- java 17 (VERSION)
- java 21 (VERSION)
- java 4 (VERSION)
- java 5 (VERSION)
- java 6 (VERSION)
- java 7 (VERSION)
- java 8 (VERSION)
- java lts (VERSION)
- java-11 (VERSION)
- java-17 (VERSION)
- java-21 (VERSION)
- java-4 (VERSION)
- java-5 (VERSION)
- java-6 (VERSION)
- java-7 (VERSION)
- java-8 (VERSION)
- java11 (VERSION)
- java17 (VERSION)
- java21 (VERSION)
- java4 (VERSION)
- java5 (VERSION)
- java6 (VERSION)
- java7 (VERSION)
- java8 (VERSION)
- jdk 11 (VERSION)
- jdk 17 (VERSION)
- jdk 21 (VERSION)
- jdk 4 (VERSION)
- jdk 5 (VERSION)
- jdk 6 (VERSION)
- jdk 7 (VERSION)
- jdk 8 (VERSION)
- jdk11 (VERSION)
- jdk17 (VERSION)
- jdk21 (VERSION)
- jdk4 (VERSION)
- jdk5 (VERSION)
- jdk6 (VERSION)
- jdk7 (VERSION)
- jdk8 (VERSION)
- jvm21 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Oracle
- License
- other_open
- Year introduced
- 1995
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 21
Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.
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)
-
Java Language and JVM Catalog dimension db id 279
Library dimension (catalog)
Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer
-
Kotlin and Java Catalog dimension db id 161
Library dimension (catalog)
Roles linked in library: Android Developer
-
Native Mobile Languages Catalog dimension db id 274
Library dimension (catalog)
Roles linked in library: Native Mobile Developer
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
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 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 |
|---|---|---|---|
|
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- 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
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
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
- Apache Flink (VERSION)
- Flink (CANONICAL)
- Flink 1.20 (VERSION)
- Flink 1.20.x (VERSION)
- Flink 1.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Stream Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 1.20
Maturity reasoning: Apache Flink appears in many data/streaming job postings and is a standard choice alongside Kafka/Spark for real-time ETL; its GitHub and vendor ecosystem remain active, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 94
- 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
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
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 |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
- 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
- Cassandra (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Wide Column Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2008
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Apache Cassandra appears in many production data-platform JDs and is a common choice for high-write, distributed workloads; GitHub and vendor docs show sustained activity rather than sunset signals.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 31
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | 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
- AWS (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.
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 & Managed Services Catalog dimension db id 221
Library dimension (catalog)
Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js 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 & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
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) |
Aliases — catalog
- Google Cloud Platform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- License
- other_open
- Year introduced
- 2008
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: GCP appears in many cloud-engineering job descriptions alongside AWS/Azure, and Google continues expanding managed services and certifications, indicating broad hiring demand rather than niche use.
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 & Hosting Providers Catalog dimension db id 414
Library dimension (catalog)
Roles linked in library: PHP Backend Developer
-
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 & Hosting Providers
cloud-hosting-providers
|
✓ | — | 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) |
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
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
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- License
- proprietary
- Year introduced
- 2011
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 118
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Data Warehouses Catalog dimension db id 22
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Tableau (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- Power BI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Microsoft
- License
- proprietary
- Year introduced
- 2015
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Looker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Bi Analytics Platform
- Vendor
- Google Cloud
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 111
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
BI and Visualization Tools Catalog dimension db id 31
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Docker (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Containerization Tool
- Vendor
- Docker, Inc.
- License
- apache_2
- Year introduced
- 2013
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 63
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Containerization and Image Builds Catalog dimension db id 152
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Cloud Platforms Catalog dimension db id 418
Library dimension (catalog)
Roles linked in library: Ruby Backend Developer
-
Deployment and Runtime Configuration Catalog dimension db id 13
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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) |
Aliases — catalog
- Apache Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Event Streaming Tool
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Apache Kafka is broadly adopted in production and appears frequently in job descriptions for event streaming, data pipelines, and microservices; it remains a common hiring-pipeline staple across backend and platform roles.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 128
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
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 |
|---|---|---|---|
|
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Scrum (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Scrum
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Scrum appears in a large share of agile project-management and product-owner job descriptions, and Scrum Alliance/PSM certifications are widely requested in hiring pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3627
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Software Concepts, Patterns & Practices Catalog dimension db id 478
Library dimension (catalog)
Roles linked in library: Engineering Manager
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | 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
- Practices
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Java | in_db |
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Apache Hadoop | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Flink | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Flink | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension 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) | |
| MongoDB | in_db |
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Cassandra | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | 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) | |
| 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) | |
| Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Azure | in_db |
Cloud Platforms & Managed Services
cloud-platforms-managed-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google Cloud Platform | in_db |
Cloud & Hosting Providers
cloud-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google Cloud Platform | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google Cloud Platform | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Google Cloud Platform | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| BigQuery | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Tableau | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Power BI | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Looker | in_db |
BI and Visualization Tools
bi-and-visualization-tools
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Docker | in_db |
Containerization and Image Builds
containerization-and-image-builds
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Deployment and Cloud Platforms
deployment-and-cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Docker | in_db |
Deployment and Runtime Configuration
deployment-and-runtime-configuration
|
✓ | — | 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) | |
| Apache Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Scrum | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Scrum | in_db |
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | 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=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | AWS Glue | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Apache Storm | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Kanban | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Apache Hadoop ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Bachelor\u0027s or Master\u0027s degree in Computer Science, Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 3,
"raw": "3 to 6 years"
},
"job_locations": [
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Data Engineer (Java Backend)",
"role_aliases": [
"Data Engineer",
"Java Data Engineer",
"Backend Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Design, develop, and maintain",
"last_5_words": "and future reference."
},
"text": "1. Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies.\n2. Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.\n3. Optimise and fine-tune existing data pipelines for improved performance and reliability.\n4. Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems.\n5. Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.\n6. Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations.\n7. Stay up-to-date with emerging technologies and trends in data engineering and incorporate them into our data infrastructure and processes.\n8. Document design specifications, technical solutions, and best practices for knowledge sharing and future reference.",
"word_count": 198
},
{
"bullet_count": 10,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Bachelor\u0027s or Master\u0027s degree",
"last_5_words": "and deliver results in a fast-paced"
},
"text": "1. Bachelor\u0027s or Master\u0027s degree in Computer Science, Engineering, or a related field.\n2. 3 to 6 years of professional experience in software development with a focus on data engineering.\n3. Strong proficiency in Java programming language, with hands-on experience in building scalable applications and data pipelines.\n4. Experience with distributed computing frameworks such as Apache Hadoop, Spark, or Flink.\n5. Proficiency in SQL and experience with relational databases like MySQL, PostgreSQL, or Oracle.\n6. Experience with NoSQL databases such as MongoDB, Cassandra, or Redis is a plus.\n7. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, and experience with related services like AWS Glue, Azure Data Factory, or Google BigQuery.\n8. Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and implement effective solutions.\n9. Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.\n10. Proven ability to manage multiple priorities and deliver results in a fast-paced and dynamic environment.",
"word_count": 218
},
{
"bullet_count": 4,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Experience with data visualisation",
"last_5_words": "or agile software development methodologies"
},
"text": "1. Experience with data visualisation tools such as Tableau, Power BI, or Looker.\n2. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.\n3. Knowledge of stream processing frameworks such as Apache Kafka or Apache Storm.\n4. Experience with agile software development methodologies like Scrum or Kanban.",
"word_count": 64
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": false,
"skill_name": "Apache Hadoop"
},
{
"is_primary": false,
"skill_name": "Spark"
},
{
"is_primary": false,
"skill_name": "Flink"
},
{
"is_primary": true,
"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": "MongoDB"
},
{
"is_primary": false,
"skill_name": "Cassandra"
},
{
"is_primary": false,
"skill_name": "Redis"
},
{
"is_primary": false,
"skill_name": "AWS"
},
{
"is_primary": false,
"skill_name": "Azure"
},
{
"is_primary": false,
"skill_name": "Google Cloud Platform"
},
{
"is_primary": false,
"skill_name": "AWS Glue"
},
{
"is_primary": false,
"skill_name": "Azure Data Factory"
},
{
"is_primary": false,
"skill_name": "BigQuery"
},
{
"is_primary": false,
"skill_name": "Tableau"
},
{
"is_primary": false,
"skill_name": "Power BI"
},
{
"is_primary": false,
"skill_name": "Looker"
},
{
"is_primary": false,
"skill_name": "Docker"
},
{
"is_primary": false,
"skill_name": "Kubernetes"
},
{
"is_primary": false,
"skill_name": "Apache Kafka"
},
{
"is_primary": false,
"skill_name": "Apache Storm"
},
{
"is_primary": false,
"skill_name": "Scrum"
},
{
"is_primary": false,
"skill_name": "Kanban"
}
],
"jd_role": {
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"rationale": null,
"role_aliases": [
"Data Engineer",
"Java Data Engineer",
"Backend Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Bachelor\u0027s or Master\u0027s degree in Computer Science, Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 3,
"raw": "3 to 6 years"
},
"job_locations": [
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "Data Engineer (Java Backend)",
"role_aliases": [
"Data Engineer",
"Java Data Engineer",
"Backend Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Design, develop, and maintain",
"last_5_words": "and future reference."
},
"text": "1. Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies.\n2. Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.\n3. Optimise and fine-tune existing data pipelines for improved performance and reliability.\n4. Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems.\n5. Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.\n6. Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations.\n7. Stay up-to-date with emerging technologies and trends in data engineering and incorporate them into our data infrastructure and processes.\n8. Document design specifications, technical solutions, and best practices for knowledge sharing and future reference.",
"word_count": 198
},
{
"bullet_count": 10,
"heading": "Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Bachelor\u0027s or Master\u0027s degree",
"last_5_words": "and deliver results in a fast-paced"
},
"text": "1. Bachelor\u0027s or Master\u0027s degree in Computer Science, Engineering, or a related field.\n2. 3 to 6 years of professional experience in software development with a focus on data engineering.\n3. Strong proficiency in Java programming language, with hands-on experience in building scalable applications and data pipelines.\n4. Experience with distributed computing frameworks such as Apache Hadoop, Spark, or Flink.\n5. Proficiency in SQL and experience with relational databases like MySQL, PostgreSQL, or Oracle.\n6. Experience with NoSQL databases such as MongoDB, Cassandra, or Redis is a plus.\n7. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, and experience with related services like AWS Glue, Azure Data Factory, or Google BigQuery.\n8. Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and implement effective solutions.\n9. Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.\n10. Proven ability to manage multiple priorities and deliver results in a fast-paced and dynamic environment.",
"word_count": 218
},
{
"bullet_count": 4,
"heading": "Preferred Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "1. Experience with data visualisation",
"last_5_words": "or agile software development methodologies"
},
"text": "1. Experience with data visualisation tools such as Tableau, Power BI, or Looker.\n2. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.\n3. Knowledge of stream processing frameworks such as Apache Kafka or Apache Storm.\n4. Experience with agile software development methodologies like Scrum or Kanban.",
"word_count": 64
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "26a5dd1d-ddb6-49ce-b8db-c77c2abd8359",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems.",
"similarity": 0.6999
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.",
"similarity": 0.6871
},
{
"kra_text": "Implements data quality validation rules, reconciliation checks, and anomaly detection to ensure data completeness, accuracy, and consistency.",
"sentence": "Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.",
"similarity": 0.6772
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6881,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Optimise and fine-tune existing data pipelines for improved performance and reliability.",
"similarity": 0.544
},
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.",
"similarity": 0.5249
},
{
"kra_text": "Responds to deployment failures, infrastructure incidents, and environment misconfiguration issues to restore service availability and prevent recurrence.",
"sentence": "Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations.",
"similarity": 0.508
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.5256,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.",
"similarity": 0.608
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.",
"similarity": 0.4914
},
{
"kra_text": "translate product and design requirements",
"sentence": "Document design specifications, technical solutions, and best practices for knowledge sharing and future reference.",
"similarity": 0.4602
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5198,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems.",
"similarity": 0.5163
},
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.",
"similarity": 0.4947
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Optimise and fine-tune existing data pipelines for improved performance and reliability.",
"similarity": 0.4733
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4948,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Backend Developer",
"kra_matches": [
{
"kra_text": "Investigates and resolves production incidents, API bugs, and service degradation through root cause analysis, hotfixes, and post-mortems.",
"sentence": "Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations.",
"similarity": 0.5308
},
{
"kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
"sentence": "Optimise and fine-tune existing data pipelines for improved performance and reliability.",
"similarity": 0.4982
},
{
"kra_text": "Implements request validation, structured error handling, and input sanitization across backend services to ensure predictable and secure API behavior.",
"sentence": "Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.",
"similarity": 0.4545
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 1,
"score": 0.4945,
"slug": "backend-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Java",
"SQL"
],
"role_id": 121,
"score": 0.6667,
"slug": "engineering-manager",
"total_count": 3
},
{
"display_name": "Pega Developer",
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"matched_count": 2,
"matched_skills": [
"Java",
"SQL"
],
"role_id": 24,
"score": 0.6667,
"slug": "pega-developer",
"total_count": 3
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Java",
"SQL"
],
"role_id": 2,
"score": 0.6667,
"slug": "data-engineer",
"total_count": 3
},
{
"display_name": "Fullstack Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Java"
],
"role_id": 15,
"score": 0.3333,
"slug": "full-stack-engineer",
"total_count": 3
},
{
"display_name": "Android Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Java"
],
"role_id": 4,
"score": 0.3333,
"slug": "android-engineer",
"total_count": 3
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.99,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.99,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Data Pipeline Engineering",
"ETL Development",
"Data Infrastructure Engineering",
"Pipeline Performance Optimization",
"Data Quality and Monitoring",
"Cloud Data Engineering",
"Cross-functional Collaboration",
"Troubleshooting and Reliability"
],
"matched_kras": [
"Design, develop, and maintain scalable and efficient data pipelines",
"Collaborate with cross-functional teams",
"Optimise and fine-tune existing data pipelines",
"Build and maintain data infrastructure",
"Ensure data quality and integrity",
"Troubleshoot data-related issues",
"Document design specifications, technical solutions, and best practices"
],
"matched_skills": [
"Java",
"data pipelines",
"ETL",
"Apache Hadoop",
"Spark",
"Flink",
"SQL",
"MySQL",
"PostgreSQL",
"Oracle",
"MongoDB",
"Cassandra",
"Redis",
"AWS",
"Azure"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 428,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 19762,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19764,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Apache Hadoop",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19766,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Oracle",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19768,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "AWS Glue",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19769,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19770,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Apache Storm",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19771,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Kanban",
"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": 1,
"existing_alias_text": "Java",
"input_term": "Java",
"matched_canonical": {
"category_id": 6,
"display_name": "Java",
"id": 1,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "java",
"sub_category_id": 96,
"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": 2010,
"existing_alias_text": "Hadoop",
"input_term": "Apache Hadoop",
"matched_canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1999,
"existing_alias_text": "Flink",
"input_term": "Flink",
"matched_canonical": {
"category_id": 5,
"display_name": "Flink",
"id": 1349,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "flink",
"sub_category_id": 94,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 271,
"existing_alias_text": "SQL",
"input_term": "SQL",
"matched_canonical": {
"category_id": 6,
"display_name": "SQL",
"id": 101,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "sql",
"sub_category_id": 97,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 134,
"existing_alias_text": "MySQL",
"input_term": "MySQL",
"matched_canonical": {
"category_id": 3,
"display_name": "MySQL",
"id": 17,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "mysql",
"sub_category_id": 29,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Power BI",
"alias_type": "CANONICAL",
"id": 360,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Power BI",
"id": 151,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "power-bi",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"input_skill": "Power BI",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Power BI",
"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": "Looker",
"alias_type": "CANONICAL",
"id": 361,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Looker",
"id": 152,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "looker",
"sub_category_id": 111,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "BI and Visualization Tools",
"id": 31,
"rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
"slug": "bi-and-visualization-tools",
"source": "db"
},
"input_skill": "Looker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Looker",
"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": "Docker",
"alias_type": "CANONICAL",
"id": 198,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Docker",
"id": 61,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "docker",
"sub_category_id": 63,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Builds",
"id": 152,
"rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
"slug": "containerization-and-image-builds",
"source": "db"
},
"input_skill": "Docker",
"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": "Deployment and Cloud Platforms",
"id": 418,
"rationale": "Platform-as-a-Service and container environments for deploying Ruby applications.",
"slug": "deployment-and-cloud-platforms",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Runtime Configuration",
"id": 13,
"rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
"slug": "deployment-and-runtime-configuration",
"source": "db"
},
"input_skill": "Docker",
"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": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Docker",
"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": "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
},
{
"aliases_in_db": [
{
"alias_text": "Apache Kafka",
"alias_type": "CANONICAL",
"id": 349,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Apache Kafka",
"id": 145,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "apache-kafka",
"sub_category_id": 128,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Event Streaming",
"id": 8,
"rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"input_skill": "Apache Kafka",
"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": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Apache Kafka",
"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": "Apache Storm",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "apache-storm",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Scrum",
"alias_type": "CANONICAL",
"id": 1851,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Scrum",
"id": 1215,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "scrum",
"sub_category_id": 3627,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Scrum",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
"id": 478,
"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
"slug": "software-concepts-patterns-practices",
"source": "db"
},
"input_skill": "Scrum",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "Scrum",
"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": "Kanban",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Practices",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "kanban",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"ETL",
"Oracle",
"AWS Glue",
"Azure Data Factory",
"Apache Storm",
"Kanban"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Apache Hadoop",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Flink",
"tag": "in_db"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "MySQL",
"tag": "in_db"
},
{
"skill": "PostgreSQL",
"tag": "in_db"
},
{
"skill": "Oracle",
"tag": "new"
},
{
"skill": "MongoDB",
"tag": "in_db"
},
{
"skill": "Cassandra",
"tag": "in_db"
},
{
"skill": "Redis",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Google Cloud Platform",
"tag": "in_db"
},
{
"skill": "AWS Glue",
"tag": "new"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "BigQuery",
"tag": "in_db"
},
{
"skill": "Tableau",
"tag": "in_db"
},
{
"skill": "Power BI",
"tag": "in_db"
},
{
"skill": "Looker",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Apache Kafka",
"tag": "in_db"
},
{
"skill": "Apache Storm",
"tag": "new"
},
{
"skill": "Scrum",
"tag": "in_db"
},
{
"skill": "Kanban",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Java Language and JVM",
"id": 279,
"rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
"slug": "java-language-and-jvm",
"source": "db"
},
"dimension_id": 279,
"input_skill": "Java",
"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": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Kotlin and Java",
"id": 161,
"rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
"slug": "kotlin-and-java",
"source": "db"
},
"dimension_id": 161,
"input_skill": "Java",
"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": "Android Developer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Native Mobile Languages",
"id": 274,
"rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
"slug": "native-mobile-languages",
"source": "db"
},
"dimension_id": 274,
"input_skill": "Java",
"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": "Native Mobile Developer",
"id": 75,
"rationale": null,
"role_archetype": "Engineering",
"slug": "native-mobile-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "Java",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Java",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
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