Pipeline run
e13504ac-8d10-421c-aa7d-e7b997c4d80d
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 large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches 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
software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. • Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation • Strong experience in programming languages Java/J2EE/Scala. • Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. • Experience with Batch Processing and AutoSys Job Scheduling and Monitoring • Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) • Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed • Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc • Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD
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 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- J2EE (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Enterprise Java Framework
- Vendor
- Oracle
- License
- other_open
- Year introduced
- 1999
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: J2EE is largely superseded by Jakarta EE/Spring in modern JDs; Oracle’s Java EE/J2EE branding was replaced and current job postings rarely ask for J2EE specifically.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- DEPRECATED
- Typical lifespan
- SHORT_LIVED
- Category id
- 5
- Sub-category id
- 1172
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Scala (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- EPFL
- License
- apache_2
- Year introduced
- 2004
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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
- 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
|
✓ | ✓ | 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
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
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Message Brokers
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- HBase (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Wide Column Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: HBase appears in a limited set of big-data/legacy Hadoop job postings, while newer JDs more often specify DynamoDB, Bigtable, or Cassandra; its market demand is specialized rather than broad.
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
- Hive (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Local Key Value Store
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2242
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Local Persistence and Offline Behavior Catalog dimension db id 85
Library dimension (catalog)
Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- NoSQL (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Database Paradigm
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1019
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
NoSQL Databases Catalog dimension db id 19
Library dimension (catalog)
Roles linked in library: Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- 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
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Elasticsearch (CANONICAL) primary
- ES (VERSION)
- ElasticSearch (VERSION)
- Elasticsearch 6 (VERSION)
- Elasticsearch 6.x (VERSION)
- Elasticsearch 7 (VERSION)
- Elasticsearch 7.x (VERSION)
- Elasticsearch 8 (VERSION)
- Elasticsearch 8.x (VERSION)
- Elasticsearch v6 (VERSION)
- Elasticsearch v7 (VERSION)
- Elasticsearch v8 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Search Datastore
- Vendor
- Elastic NV
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.93
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 8.x
Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 2925
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Magento Search and Indexing Catalog dimension db id 403
Library dimension (catalog)
Roles linked in library: Magento Dev
-
Search and Content Discovery Catalog dimension db id 356
Library dimension (catalog)
Roles linked in library: Drupal Dev, Sitecore Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- 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
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- 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
- Operating Systems
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- Git (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Version Control Tool
- Vendor
- Linus Torvalds
- License
- gpl_v2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 730
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
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) |
Aliases — catalog
- Jenkins (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Ci Cd Tool
- Vendor
- CloudBees
- License
- mit
- Year introduced
- 2011
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Jenkins remains a common CI/CD requirement in job postings and enterprise DevOps stacks, with broad plugin ecosystem and long-running GitHub activity despite newer alternatives.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 184
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| J2EE | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Scala | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Scala | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| HBase | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hive | in_db |
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| NoSQL | in_db |
NoSQL Databases
nosql-databases
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| 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 for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Elasticsearch | in_db |
Magento Search and Indexing
magento-search-and-indexing
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Elasticsearch | in_db |
Search and Content Discovery
search-and-content-discovery
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Git | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Jenkins | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Jenkins | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | YARN | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Confluent Kafka | type=Message Brokers subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Impala | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Batch Processing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | AutoSys | type=Infrastructure Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Cloudera | type=Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Sqoop | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Linux | type=Operating Systems subtype=general nature=TOOL lifespan=EVERGREEN |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "fail",
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Scala",
"J2EE",
"Hive",
"Jenkins",
"Monitoring",
"Git",
"CI/CD",
"NoSQL",
"Hadoop",
"HBase",
"Java",
"SQL",
"Elasticsearch",
"Kafka"
],
"role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "J2EE"
},
{
"is_primary": true,
"skill_name": "Scala"
},
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": true,
"skill_name": "Hadoop"
},
{
"is_primary": true,
"skill_name": "HDFS"
},
{
"is_primary": true,
"skill_name": "YARN"
},
{
"is_primary": true,
"skill_name": "Confluent Kafka"
},
{
"is_primary": true,
"skill_name": "HBase"
},
{
"is_primary": true,
"skill_name": "Hive"
},
{
"is_primary": true,
"skill_name": "Impala"
},
{
"is_primary": true,
"skill_name": "NoSQL"
},
{
"is_primary": true,
"skill_name": "Batch Processing"
},
{
"is_primary": true,
"skill_name": "AutoSys"
},
{
"is_primary": true,
"skill_name": "Cloudera"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "Elasticsearch"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Sqoop"
},
{
"is_primary": true,
"skill_name": "Oracle"
},
{
"is_primary": true,
"skill_name": "Linux"
},
{
"is_primary": true,
"skill_name": "Git"
},
{
"is_primary": true,
"skill_name": "Jenkins"
},
{
"is_primary": true,
"skill_name": "CI/CD"
}
],
"jd_role": null,
"nano_parsed": {
"JD_type": "fail",
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Scala",
"J2EE",
"Hive",
"Jenkins",
"Monitoring",
"Git",
"CI/CD",
"NoSQL",
"Hadoop",
"HBase",
"Java",
"SQL",
"Elasticsearch",
"Kafka"
],
"role_archetype": "Engineering"
},
"rejected": false,
"rejection_reason": null,
"run_id": "e13504ac-8d10-421c-aa7d-e7b997c4d80d",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
"similarity": 0.633
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.633,
"slug": "data-engineer",
"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": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
"similarity": 0.4131
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4131,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
"similarity": 0.4052
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4052,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements security controls including SIEM integration, endpoint detection and response, identity management, and firewall rule management.",
"sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
"similarity": 0.3907
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 5,
"score": 0.3907,
"slug": "cybersecurity-engineer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Designs multi-region and multi-availability-zone cloud infrastructure architectures for high availability, fault tolerance, and horizontal scalability.",
"sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
"similarity": 0.384
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.384,
"slug": "cloud-architect",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"Apache Spark",
"Hadoop",
"Java",
"SQL",
"Scala"
],
"role_id": 2,
"score": 0.2083,
"slug": "data-engineer",
"total_count": 24
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"CI/CD",
"Jenkins",
"Scala"
],
"role_id": 3,
"score": 0.125,
"slug": "ml-engineer",
"total_count": 24
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"J2EE",
"Java",
"NoSQL"
],
"role_id": 1,
"score": 0.125,
"slug": "backend-engineer",
"total_count": 24
},
{
"display_name": "Android Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Hive",
"Java"
],
"role_id": 4,
"score": 0.0833,
"slug": "android-engineer",
"total_count": 24
},
{
"display_name": "DevOps Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"CI/CD",
"Jenkins"
],
"role_id": 10,
"score": 0.0833,
"slug": "devops-engineer",
"total_count": 24
}
]
},
"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": 0.98,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.98,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Large-scale distributed data processing",
"Big data platform engineering",
"Batch processing and job scheduling",
"Performance troubleshooting and cluster operations",
"Hadoop ecosystem engineering",
"Data security, management, and governance"
],
"matched_kras": [
"Building large scale distributed data processing systems/application",
"Develop Big Data solution at enterprise scale",
"End to end implementation",
"Work with Cloudera on open issues",
"Implement cluster configuration changes as needed",
"Performance analysis, troubleshooting and resolution",
"Knowledge of Hadoop Security, Data Management and Governance"
],
"matched_skills": [
"Java",
"J2EE",
"Scala",
"Spark",
"Hadoop",
"HDFS",
"YARN",
"Confluent Kafka",
"Hbase",
"Hive",
"Impala",
"NoSQL",
"AutoSys",
"Cloudera",
"Elasticsearch",
"Sqoop",
"Oracle",
"Linux",
"Git",
"Jenkins CI/CD"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches Data Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 160,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 8461,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "HDFS",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8462,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "YARN",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8463,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Confluent Kafka",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8464,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Impala",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8465,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Batch Processing",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8466,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "AutoSys",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8467,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Cloudera",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8468,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8469,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Sqoop",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8470,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Oracle",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 8471,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Linux",
"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": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2511,
"existing_alias_text": "J2EE",
"input_term": "J2EE",
"matched_canonical": {
"category_id": 5,
"display_name": "J2EE",
"id": 1565,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "j2ee",
"sub_category_id": 1172,
"typical_lifespan": "SHORT_LIVED",
"volatility": "DEPRECATED"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 272,
"existing_alias_text": "Scala",
"input_term": "Scala",
"matched_canonical": {
"category_id": 6,
"display_name": "Scala",
"id": 102,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "scala",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 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": 2010,
"existing_alias_text": "Hadoop",
"input_term": "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": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2011,
"existing_alias_text": "HBase",
"input_term": "HBase",
"matched_canonical": {
"category_id": 3,
"display_name": "HBase",
"id": 1352,
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"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"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "SQL",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "SQL",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Elasticsearch",
"alias_type": "CANONICAL",
"id": 4648,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ES",
"alias_type": "VERSION",
"id": 4649,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "ElasticSearch",
"alias_type": "VERSION",
"id": 4650,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6",
"alias_type": "VERSION",
"id": 4651,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 6.x",
"alias_type": "VERSION",
"id": 4657,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7",
"alias_type": "VERSION",
"id": 4652,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 7.x",
"alias_type": "VERSION",
"id": 4658,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8",
"alias_type": "VERSION",
"id": 4653,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch 8.x",
"alias_type": "VERSION",
"id": 4659,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v6",
"alias_type": "VERSION",
"id": 4654,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v7",
"alias_type": "VERSION",
"id": 4655,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Elasticsearch v8",
"alias_type": "VERSION",
"id": 4656,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 3,
"display_name": "Elasticsearch",
"id": 3171,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "elasticsearch",
"sub_category_id": 2925,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Magento Search and Indexing",
"id": 403,
"rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
"slug": "magento-search-and-indexing",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Magento Dev",
"id": 231,
"rationale": null,
"role_archetype": "Engineering",
"slug": "magento-dev",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Search and Content Discovery",
"id": 356,
"rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
"slug": "search-and-content-discovery",
"source": "db"
},
"input_skill": "Elasticsearch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"source": "db"
},
{
"display_name": "Sitecore Dev",
"id": 233,
"rationale": null,
"role_archetype": "Engineering",
"slug": "sitecore-dev",
"source": "db"
}
]
}
],
"input_skill": "Elasticsearch",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ETL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "etl",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Sqoop",
"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": "sqoop",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Oracle",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "oracle",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Linux",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Operating Systems",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "linux",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Git",
"alias_type": "CANONICAL",
"id": 1613,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Git",
"id": 1002,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "git",
"sub_category_id": 730,
"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": "Git",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Git",
"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": "Jenkins",
"alias_type": "CANONICAL",
"id": 544,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Jenkins",
"id": 283,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "jenkins",
"sub_category_id": 184,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "Jenkins",
"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": "CI/CD for Machine Learning",
"id": 56,
"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
"slug": "ci-cd-for-machine-learning",
"source": "db"
},
"input_skill": "Jenkins",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "Jenkins",
"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": "CI/CD",
"alias_type": "CANONICAL",
"id": 1826,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "CI/CD",
"id": 1190,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "ci-cd",
"sub_category_id": 900,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "CI/CD",
"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": "CI/CD for Machine Learning",
"id": 56,
"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
"slug": "ci-cd-for-machine-learning",
"source": "db"
},
"input_skill": "CI/CD",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "CI/CD",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"HDFS",
"YARN",
"Confluent Kafka",
"Impala",
"Batch Processing",
"AutoSys",
"Cloudera",
"ETL",
"Sqoop",
"Oracle",
"Linux"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches Data Engineer.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "J2EE",
"tag": "in_db"
},
{
"skill": "Scala",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "HDFS",
"tag": "new"
},
{
"skill": "YARN",
"tag": "new"
},
{
"skill": "Confluent Kafka",
"tag": "new"
},
{
"skill": "HBase",
"tag": "in_db"
},
{
"skill": "Hive",
"tag": "in_db"
},
{
"skill": "Impala",
"tag": "new"
},
{
"skill": "NoSQL",
"tag": "in_db"
},
{
"skill": "Batch Processing",
"tag": "new"
},
{
"skill": "AutoSys",
"tag": "new"
},
{
"skill": "Cloudera",
"tag": "new"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Elasticsearch",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Sqoop",
"tag": "new"
},
{
"skill": "Oracle",
"tag": "new"
},
{
"skill": "Linux",
"tag": "new"
},
{
"skill": "Git",
"tag": "in_db"
},
{
"skill": "Jenkins",
"tag": "in_db"
},
{
"skill": "CI/CD",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"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 for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Java",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
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"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"dimension_id": 2,
"input_skill": "J2EE",
"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": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1565,
"skill_tag": "in_db",
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{
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"dimension": {
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"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Scala",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
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],
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"skill_tag": "in_db",
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{
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"dimension": {
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"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
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"slug": "ml-engineer",
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},
{
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"slug": "ml-ops-engineer",
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],
"skill_dimension_saved": true,
"skill_id": 102,
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{
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"dimension": {
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"slug": "etl-and-elt-tooling",
"source": "db"
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"dimension_id": 24,
"input_skill": "Spark",
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
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}
],
"skill_dimension_saved": true,
"skill_id": 1350,
"skill_tag": "in_db",
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{
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"source": "db"
},
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"matched_chosen_role": true,
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"role_dimension_saved": true,
"roles_from_db": [
{
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"id": 2,
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"role_archetype": null,
"slug": "data-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 1351,
"skill_tag": "in_db",
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{
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"slug": "cloud-storage-and-data-services",
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},
"dimension_id": 144,
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"role_dimension_saved": false,
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{
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],
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{
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"slug": "local-persistence-and-offline-behavior",
"source": "db"
},
"dimension_id": 85,
"input_skill": "Hive",
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"matched_chosen_role": false,
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"role_dimension_saved": false,
"roles_from_db": [
{
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"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Flutter Developer",
"id": 74,
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"role_archetype": "Engineering",
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{
"display_name": "Hybrid Mobile Developer",
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"rationale": null,
"role_archetype": null,
"slug": "hybrid-mobile-developer",
"source": "db"
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{
"display_name": "Native Mobile Developer",
"id": 75,
"rationale": null,
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{
"display_name": "React Native Developer",
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},
{
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"rationale": null,
"role_archetype": null,
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"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2754,
"skill_tag": "in_db",
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{
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"matched_chosen_role": false,
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{
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],
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{
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},
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"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": [
{
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],
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"skill_id": 101,
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},
{
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},
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"role_dimension_saved": true,
"roles_from_db": [
{
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],
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"skill_id": 101,
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{
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},
"dimension_id": 403,
"input_skill": "Elasticsearch",
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"matched_chosen_role": false,
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"role_dimension_saved": false,
"roles_from_db": [
{
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"rationale": null,
"role_archetype": "Engineering",
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}
],
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{
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},
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{
"display_name": "Drupal Dev",
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"rationale": null,
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{
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],
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{
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},
"dimension_id": 96,
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"skill_dimension_saved": true,
"skill_id": 1002,
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{
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},
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"matched_chosen_role": false,
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{
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],
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{
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],
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
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],
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{
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"roles_from_db": [
{
"display_name": "ML Engineer",
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}
],
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"skill_tag": "in_db",
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}
],
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"skill_dimension_saved": 0,
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},
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.