Pipeline run
c29ce8e3-5fd0-4072-ae75-004f3c09d201
Pipeline LLM cost (USD)
API 1: $0.0085
API 2: $0.0003
API 3: $0.0000
Total: $0.0089
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded
sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work
· Data transformation and modeling
Build and maintain Hadoop/Spark data solutions and warehouses, using Java, HiveQL, Pig, Sqoop, HBase and shell scripts to model data, support dashboards, and run/repair scheduled refreshes with incident handling and documentation.
"Design & develop data flows, data models & data warehouses / data solutions"
Tech stack maturity
Mainstream Legacy
The skill set centers on Hadoop, HBase, Java, Spark, and traditional testing/review practices, which is characteristic of established big-data enterprise stacks rather than cloud-native or bleeding-edge platforms.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
—
Evidence — skills matched in JD (19)
Hadoop
Spark
Java
Unix shell scripting
Sqoop
Pig
HiveQL
UDF
HBase
Unit Testing
Integration Testing
Code Review
Agile
Scrum
Eclipse
MapReduce
Business Intelligence
Design Review
Iterative Development
Skill cluster (4 dimension groups, role-scoped)
ETL and ELT Tooling
Hadoop
Spark
Programming Languages for Data Work
Java
Testing and Quality Assurance
Integration Testing
Cross-cutting / unaligned
Unix shell scripting
Sqoop
Pig
HiveQL
UDF
HBase
Unit Testing
Code Review
Agile
Scrum
Eclipse
MapReduce
Business Intelligence
Design Review
Iterative Development
Show KRA description ↓
• Design & develop data flows, data models & data warehouses / data solutions
• Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports
• Provide user support including incident management, data issues & maintenance of daily / weekly data refresh schedules & on-call responsibilities to meet business SLA’s
• Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams
• Minimum 3 years of IT experience working on relevant technologies
• Must have extensive hands on experience in designing, developing, and maintaining software solutions in Hadoop cluster
• Must have working experience in Spark with JAVA
• Must demonstrate Hadoop best practices
• Must have experience with strong UNIX shell scripting, SQOOP, eclipse (Any IDE)
• Must have experience with Developing Pig scripts/Hive QL, UDF for analyzing all semi-structured / unstructured / structured data flows
• Must have experience with NoSQL Databases such as HBASE
• Demonstrates broad knowledge of technical solutions, design patterns, and code for medium/complex applications deployed in Hadoop production
• Good to have working experience with Developing MapReduce programs running on the Hadoop cluster using Java
• Good to have working experience in the data warehousing and Business Intelligence systems
• Participate in design reviews, code reviews, unit testing and integration testing
• Assume ownership and accountability for the assigned deliverables through all phases of the development lifecycle
• SDLC Methodology (Agile / Scrum / Iterative Development)
Signals
Skill
engineering-manager
0.21
Alias
data-engineer
1.00
KRA
data-engineer
0.57
Post-classification
Centroidupdated · n=349
Alias collision log—
New-role queue—
New skills captured9
New KRA captured—
Captured for admin review
Unix shell scripting
primary
↔
Data Engineer
pending
Sqoop
primary
↔
Data Engineer
pending
Pig
primary
↔
Data Engineer
pending
HiveQL
primary
↔
Data Engineer
pending
UDF
primary
↔
Data Engineer
pending
MapReduce
↔
Data Engineer
pending
Business Intelligence
↔
Data Engineer
pending
Design Review
↔
Data Engineer
pending
Iterative Development
↔
Data Engineer
pending
Status:
completed
Created: 2026-05-27T15:47:48.709686Z
Updated: 2026-06-12T15:58:20.480457Z
API 3 duration: 43250 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
Data Engineer
domain · Data Engineering & Analytics CASE DOMAINslug: data-engineer · id: 2 · source: db
Domain=Data Engineering & Analytics; The JD is centered on Hadoop/Spark-based data engineering, ETL-style data flows, warehousing support, and production data pipeline maintenance rather than pure BI or modeling work.
Matched skills
HadoopSparkJAVAUNIX shell scriptingSQOOPeclipsePig scriptsHive QLUDFHBASEMapReducedata warehousingBusiness IntelligenceAgile / Scrum / Iterative Development
Matched dimensions
Big Data Data EngineeringData Warehouse DevelopmentDashboard Data SupportProduction Support and Incident ManagementHadoop Application DevelopmentETL/Data Flow DevelopmentDocumentation and Architecture DiagramsSDLC Delivery in Agile
Matched KRAs
Design & develop data flows, data models & data warehousesSupport dashboards / reports with relevant dataProvide user support including incident managementMaintain daily / weekly data refresh schedulesDeliver documentation for build and support responsibilitiesParticipate in design reviews, code reviews, unit testingAssume ownership and accountability for assigned deliverables
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
1
Skipped
Job description
OVERVIEW The Enterprise Data Warehouse Software Engineer in Big Data is a key role for Data Services development activities required to enable Business Intelligence solutions for L Brands. Responsibilities will include providing technical solutions to complex business requirements through innovative data architecture design and data services solutions. This role requires hands-on experience in data warehouse, business intelligence, Data modeling, ETL, HADOOP/MapR technologies and data services development using SQL, SPARK with JAVA, HIVE techniques, and architecture. This role will interface and work in collaboration with a highly talented, energetic and diverse BI team and other cross functional teams (POS, finance, manufacturing, enterprise planning, HRMS, Supply-Chain and several other systems) to devise end-to-end solutions. Responsibilities • Design & develop data flows, data models & data warehouses / data solutions • Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports • Provide user support including incident management, data issues & maintenance of daily / weekly data refresh schedules & on-call responsibilities to meet business SLA’s • Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams Education Required: Bachelor’s Degree in Computer Science/Information Systems Preferred: Master’s Degree in Computer Science/Information Systems Required Skills / Qualifications • Minimum 3 years of IT experience working on relevant technologies • Must have extensive hands on experience in designing, developing, and maintaining software solutions in Hadoop cluster • Must have working experience in Spark with JAVA • Must demonstrate Hadoop best practices • Must have experience with strong UNIX shell scripting, SQOOP, eclipse (Any IDE) • Must have experience with Developing Pig scripts/Hive QL, UDF for analyzing all semi-structured / unstructured / structured data flows • Must have experience with NoSQL Databases such as HBASE • Demonstrates broad knowledge of technical solutions, design patterns, and code for medium/complex applications deployed in Hadoop production • Good to have working experience with Developing MapReduce programs running on the Hadoop cluster using Java • Good to have working experience in the data warehousing and Business Intelligence systems • Participate in design reviews, code reviews, unit testing and integration testing • Assume ownership and accountability for the assigned deliverables through all phases of the development lifecycle • SDLC Methodology (Agile / Scrum / Iterative Development)
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Hadoop
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Hadoop
id=1351
· hadoop
Aliases — catalog
- Hadoop (CANONICAL)
Context tags (catalog)
Big Data
Data Lake
Distributed Computing
ELT
ETL
Flume
HDFS
Hive
Kafka
MapReduce
NoSQL
Oozie
Pig
Spark
Sqoop
YARN
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 |
Spark
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Apache Spark
id=1350
· apache-spark
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Apache Kafka
Cluster Manager
DAGScheduler
Data Lake
DataFrame
ETL
Hadoop
MLlib
Machine Learning
PySpark
RDD
Scala
Spark SQL
Spark Streaming
SparkSession
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 |
Java
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Java
id=1
· java
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)
APIs
Apache Tomcat
Concurrency
Design patterns
Garbage collection
GraalVM
Gradle
Hibernate
JDBC
JDK
JPA
JUnit
JVM
Java 8
Java EE
JavaFX
Kafka
Lambda expressions
Maven
Microservices
Mockito
Object-oriented
REST
RESTful
SOAP
Servlets
Spring
Spring Boot
Tomcat
microservices
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 |
Unix shell scripting
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Bash scripting
id=193
· bash-scripting
Aliases — catalog
- Bash scripting (CANONICAL) primary
Context tags (catalog)
POSIX
automation
awk
bash_profile
bashrc
chmod
command_line
cron
environment variables
exit codes
functions
grep
here-doc
pipes
redirection
scripts
sed
shebang
shell
stdin
stdout
text_processing
variables
xargs
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Shell Scripting Language
- Vendor
- GNU Project
- License
- gpl_v3
- Year introduced
- 1989
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Bash scripting appears in many DevOps, SRE, and Linux admin job descriptions and remains a standard shell on Unix-like systems; no vendor sunset or clear replacement has displaced it.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 98
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Sqoop
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Eclipse
Secondary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Eclipse
id=1569
· eclipse
Aliases — catalog
- Eclipse (CANONICAL)
Context tags (catalog)
Eclipse Marketplace
Git
Gradle
JUnit
Java
Javadoc
Maven
build path
code completion
debugging
perspective
plugin
source control
syntax highlighting
workspace
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Ide
- Vendor
- Eclipse Foundation
- License
- other_open
- Year introduced
- 2001
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Eclipse still appears in some Java/embedded JDs, but JD volume is far below IntelliJ IDEA/VS Code and market share has shifted to those IDEs.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 796
- 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) |
Pig
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
HiveQL
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Query Languages
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
UDF
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
HBase
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: HBase
id=1352
· hbase
Aliases — catalog
- HBase (CANONICAL)
Context tags (catalog)
Apache
Bigtable
Hadoop
MapReduce
NoSQL
REST API
Thrift
column family
data model
data replication
distributed
real-time
region server
scalability
table design
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) |
MapReduce
Secondary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Business Intelligence
Secondary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Concepts
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Unit Testing
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Unit Testing
id=517
· unit-testing
Aliases — catalog
- Unit Testing (CANONICAL)
Context tags (catalog)
JUnit
NUnit
TDD
arrange-act-assert
assertions
code coverage
fixtures
mocking
pytest
regression
stubs
test cases
test doubles
test runner
xUnit
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Testing Methodology
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Unit testing is a standard hiring requirement across software JDs and appears in mainstream frameworks/docs; GitHub and Stack Overflow usage remain consistently high, with no successor replacing it.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 44
- 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) |
Integration Testing
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Integration testing
id=56
· integration-testing
Aliases — catalog
- Integration testing (CANONICAL) primary
- integration testing (CANONICAL)
Context tags (catalog)
API testing
CI/CD
Cucumber
JUnit
Selenium
behavior-driven development
continuous integration
contract testing
end-to-end
end-to-end testing
fixtures
mocking
pytest
quality assurance
regression testing
smoke testing
stubs
system testing
test automation
test cases
test coverage
test data
test frameworks
test harness
test strategy
test suite
test-driven development
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Testing Methodology
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Integration testing is a standard QA skill in job descriptions across backend, frontend, and DevOps roles; it’s commonly paired with CI/CD and tools like Jest, Cypress, and Testcontainers.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 44
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Testing and Defect Resolution Catalog dimension db id 262
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Testing and Quality Assurance Catalog dimension db id 12
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Testing and Defect Resolution
testing-and-defect-resolution
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Testing and Quality Assurance
testing-and-quality-assurance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Code Review
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Code Review
id=516
· code-review
Aliases — catalog
- Code Review (CANONICAL)
Context tags (catalog)
Bitbucket
GitHub
GitLab
PR review
approval workflow
branch protection
code quality
diff
inline comments
linting
merge request
pair programming
pull request
review checklist
static analysis
Stored enrichment (catalog DB)
- Category
- SoftSkill
- Sub-category
- Code Review
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 58
- Sub-category id
- 364
- 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) |
Design Review
Secondary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Soft Skills
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Agile
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Agile
id=520
· agile
Aliases — catalog
- Agile (CANONICAL) primary
Context tags (catalog)
Kanban
SAFe
Scrum
backlog
backlog grooming
burndown
burndown chart
continuous delivery
continuous improvement
cross-functional
daily standup
epics
incremental development
iteration
iteration planning
lean
product backlog
product owner
retrospective
sprint
sprint planning
stand-up
story points
user stories
velocity
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3594
- 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) |
Scrum
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: Scrum
id=1215
· scrum
Aliases — catalog
- Scrum (CANONICAL) primary
Context tags (catalog)
Agile
Kanban
backlog
burndown chart
cross-functional team
daily standup
definition of done
increment
product owner
release planning
retrospective
scrum framework
scrum master
sprint
story points
user stories
velocity
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) |
Iterative Development
Secondary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- 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 |
|---|---|---|---|---|---|---|
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| 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 | |
| Unix shell scripting | new |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Eclipse | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| HBase | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Unit Testing | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Integration Testing | in_db |
Testing and Defect Resolution
testing-and-defect-resolution
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Integration Testing | in_db |
Testing and Quality Assurance
testing-and-quality-assurance
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Code Review | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| 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 | Sqoop | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Pig | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | HiveQL | type=Query Languages subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | UDF | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | MapReduce | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Business Intelligence | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Design Review | type=Soft Skills subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Iterative Development | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Unix shell scripting ↔ Programming Languages for ML Systems |
nano JD Parser — gpt-4.1-nano click to toggle
RoleEnterprise Data Warehouse Software Engineer
CompanyL Brands
ExperienceMinimum 3 years of IT experience working on relevant technologies
DomainIT Services & Consulting
JD type
pass
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "L Brands",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science/Information Systems",
"raw": "Bachelor\u2019s Degree in Computer Science/Information Systems",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MTECH/ME - Computer Science/Information Systems",
"raw": "Master\u2019s Degree in Computer Science/Information Systems",
"requirement": "preferred"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "Minimum 3 years of IT experience working on relevant technologies"
},
"job_locations": [],
"role": "Enterprise Data Warehouse Software Engineer",
"role_aliases": [
"Data Engineer",
"Software Engineer",
"Big Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 4,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design \u0026 develop data flows,",
"last_5_words": "architecture and data flow diagrams"
},
"text": "\u2022 Design \u0026 develop data flows, data models \u0026 data warehouses / data solutions\n\u2022 Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports\n\u2022 Provide user support including incident management, data issues \u0026 maintenance of daily / weekly data refresh schedules \u0026 on-call responsibilities to meet business SLA\u2019s\n\u2022 Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"word_count": 56
},
{
"bullet_count": 12,
"heading": "Required Skills / Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Minimum 3 years of IT",
"last_5_words": "Agile / Scrum / Iterative Development"
},
"text": "\u2022 Minimum 3 years of IT experience working on relevant technologies\n\u2022 Must have extensive hands on experience in designing, developing, and maintaining software solutions in Hadoop cluster\n\u2022 Must have working experience in Spark with JAVA\n\u2022 Must demonstrate Hadoop best practices\n\u2022 Must have experience with strong UNIX shell scripting, SQOOP, eclipse (Any IDE)\n\u2022 Must have experience with Developing Pig scripts/Hive QL, UDF for analyzing all semi-structured / unstructured / structured data flows\n\u2022 Must have experience with NoSQL Databases such as HBASE\n\u2022 Demonstrates broad knowledge of technical solutions, design patterns, and code for medium/complex applications deployed in Hadoop production\n\u2022 Good to have working experience with Developing MapReduce programs running on the Hadoop cluster using Java\n\u2022 Good to have working experience in the data warehousing and Business Intelligence systems\n\u2022 Participate in design reviews, code reviews, unit testing and integration testing\n\u2022 Assume ownership and accountability for the assigned deliverables through all phases of the development lifecycle\n\u2022 SDLC Methodology (Agile / Scrum / Iterative Development)",
"word_count": 186
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Hadoop"
},
{
"is_primary": true,
"skill_name": "Spark"
},
{
"is_primary": true,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "Unix shell scripting"
},
{
"is_primary": true,
"skill_name": "Sqoop"
},
{
"is_primary": false,
"skill_name": "Eclipse"
},
{
"is_primary": true,
"skill_name": "Pig"
},
{
"is_primary": true,
"skill_name": "HiveQL"
},
{
"is_primary": true,
"skill_name": "UDF"
},
{
"is_primary": true,
"skill_name": "HBase"
},
{
"is_primary": false,
"skill_name": "MapReduce"
},
{
"is_primary": false,
"skill_name": "Business Intelligence"
},
{
"is_primary": true,
"skill_name": "Unit Testing"
},
{
"is_primary": true,
"skill_name": "Integration Testing"
},
{
"is_primary": true,
"skill_name": "Code Review"
},
{
"is_primary": false,
"skill_name": "Design Review"
},
{
"is_primary": true,
"skill_name": "Agile"
},
{
"is_primary": true,
"skill_name": "Scrum"
},
{
"is_primary": false,
"skill_name": "Iterative Development"
}
],
"jd_role": {
"display_name": "Enterprise Data Warehouse Software Engineer",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Software Engineer",
"Big Data Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "L Brands",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science/Information Systems",
"raw": "Bachelor\u2019s Degree in Computer Science/Information Systems",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MTECH/ME - Computer Science/Information Systems",
"raw": "Master\u2019s Degree in Computer Science/Information Systems",
"requirement": "preferred"
}
],
"experience": {
"max": null,
"min": 3,
"raw": "Minimum 3 years of IT experience working on relevant technologies"
},
"job_locations": [],
"role": "Enterprise Data Warehouse Software Engineer",
"role_aliases": [
"Data Engineer",
"Software Engineer",
"Big Data Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 4,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design \u0026 develop data flows,",
"last_5_words": "architecture and data flow diagrams"
},
"text": "\u2022 Design \u0026 develop data flows, data models \u0026 data warehouses / data solutions\n\u2022 Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports\n\u2022 Provide user support including incident management, data issues \u0026 maintenance of daily / weekly data refresh schedules \u0026 on-call responsibilities to meet business SLA\u2019s\n\u2022 Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"word_count": 56
},
{
"bullet_count": 12,
"heading": "Required Skills / Qualifications",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Minimum 3 years of IT",
"last_5_words": "Agile / Scrum / Iterative Development"
},
"text": "\u2022 Minimum 3 years of IT experience working on relevant technologies\n\u2022 Must have extensive hands on experience in designing, developing, and maintaining software solutions in Hadoop cluster\n\u2022 Must have working experience in Spark with JAVA\n\u2022 Must demonstrate Hadoop best practices\n\u2022 Must have experience with strong UNIX shell scripting, SQOOP, eclipse (Any IDE)\n\u2022 Must have experience with Developing Pig scripts/Hive QL, UDF for analyzing all semi-structured / unstructured / structured data flows\n\u2022 Must have experience with NoSQL Databases such as HBASE\n\u2022 Demonstrates broad knowledge of technical solutions, design patterns, and code for medium/complex applications deployed in Hadoop production\n\u2022 Good to have working experience with Developing MapReduce programs running on the Hadoop cluster using Java\n\u2022 Good to have working experience in the data warehousing and Business Intelligence systems\n\u2022 Participate in design reviews, code reviews, unit testing and integration testing\n\u2022 Assume ownership and accountability for the assigned deliverables through all phases of the development lifecycle\n\u2022 SDLC Methodology (Agile / Scrum / Iterative Development)",
"word_count": 186
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "c29ce8e3-5fd0-4072-ae75-004f3c09d201",
"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": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
"sentence": "Design \u0026 develop data flows, data models \u0026 data warehouses / data solutions",
"similarity": 0.6521
},
{
"kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
"sentence": "Provide user support including incident management, data issues \u0026 maintenance of daily / weekly data refresh schedules \u0026 on-call responsibilities to meet business SLA\u2019s",
"similarity": 0.5328
},
{
"kra_text": "Designs dimensional models, star schemas, data vault structures, and curated data mart tables to support BI tools and self-service analytics consumption.",
"sentence": "Good to have working experience in the data warehousing and Business Intelligence systems",
"similarity": 0.5199
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5682,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"similarity": 0.5651
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Participate in design reviews, code reviews, unit testing and integration testing",
"similarity": 0.5554
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Design \u0026 develop data flows, data models \u0026 data warehouses / data solutions",
"similarity": 0.4664
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.5289,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "Angular Frontend Developer",
"kra_matches": [
{
"kra_text": "collaboration with design and QA",
"sentence": "Participate in design reviews, code reviews, unit testing and integration testing",
"similarity": 0.5876
},
{
"kra_text": "collaboration with design and QA",
"sentence": "Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports",
"similarity": 0.4774
},
{
"kra_text": "collaboration with design and QA",
"sentence": "Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"similarity": 0.4738
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 90,
"score": 0.5129,
"slug": "angular-frontend-developer",
"total_count": null
},
{
"display_name": "Flutter Developer",
"kra_matches": [
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Participate in design reviews, code reviews, unit testing and integration testing",
"similarity": 0.5703
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Collaborate with report developers to source relevant data and build solution to support development of dashboards / reports",
"similarity": 0.511
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"similarity": 0.44
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.5071,
"slug": "flutter-developer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Responds to deployment failures, infrastructure incidents, and environment misconfiguration issues to restore service availability and prevent recurrence.",
"sentence": "Provide user support including incident management, data issues \u0026 maintenance of daily / weekly data refresh schedules \u0026 on-call responsibilities to meet business SLA\u2019s",
"similarity": 0.5112
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Participate in design reviews, code reviews, unit testing and integration testing",
"similarity": 0.5037
},
{
"kra_text": "Writes runbooks, deployment guides, incident playbooks, and infrastructure documentation to support operations and knowledge sharing.",
"sentence": "Deliver required documentation for build and support responsibilities, including architecture and data flow diagrams",
"similarity": 0.4795
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.4981,
"slug": "devops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Agile",
"Java",
"Scrum"
],
"role_id": 121,
"score": 0.2143,
"slug": "engineering-manager",
"total_count": 14
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Apache Spark",
"Hadoop",
"Java"
],
"role_id": 2,
"score": 0.2143,
"slug": "data-engineer",
"total_count": 14
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Integration testing",
"Java"
],
"role_id": 1,
"score": 0.1429,
"slug": "backend-engineer",
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},
{
"display_name": "Pega Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Integration testing",
"Java"
],
"role_id": 24,
"score": 0.1429,
"slug": "pega-developer",
"total_count": 14
},
{
"display_name": "Scala Backend Developer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Integration testing",
"Java"
],
"role_id": 87,
"score": 0.1429,
"slug": "scala-backend-developer",
"total_count": 14
}
]
},
"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": 0.97,
"slug": "data-engineer",
"total_count": null
},
"confidence": 0.97,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Big Data Data Engineering",
"Data Warehouse Development",
"Dashboard Data Support",
"Production Support and Incident Management",
"Hadoop Application Development",
"ETL/Data Flow Development",
"Documentation and Architecture Diagrams",
"SDLC Delivery in Agile"
],
"matched_kras": [
"Design \u0026 develop data flows, data models \u0026 data warehouses",
"Support dashboards / reports with relevant data",
"Provide user support including incident management",
"Maintain daily / weekly data refresh schedules",
"Deliver documentation for build and support responsibilities",
"Participate in design reviews, code reviews, unit testing",
"Assume ownership and accountability for assigned deliverables"
],
"matched_skills": [
"Hadoop",
"Spark",
"JAVA",
"UNIX shell scripting",
"SQOOP",
"eclipse",
"Pig scripts",
"Hive QL",
"UDF",
"HBASE",
"MapReduce",
"data warehousing",
"Business Intelligence",
"Agile / Scrum / Iterative Development"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Hadoop/Spark-based data engineering, ETL-style data flows, warehousing support, and production data pipeline maintenance rather than pure BI or modeling work.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 349,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 16408,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Sqoop",
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},
{
"is_primary": true,
"queue_id": 16410,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Pig",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 16411,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "HiveQL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 16412,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "UDF",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16413,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
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},
{
"is_primary": false,
"queue_id": 16414,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Business Intelligence",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16415,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Design Review",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 16416,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Iterative Development",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
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"alias_persisted": false,
"existing_alias_id": 2010,
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"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
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"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": 1,
"existing_alias_text": "Java",
"input_term": "Java",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "java",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 429,
"existing_alias_text": "Bash scripting",
"input_term": "Unix shell scripting",
"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "bash-scripting",
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"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": 2515,
"existing_alias_text": "Eclipse",
"input_term": "Eclipse",
"matched_canonical": {
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"display_name": "Eclipse",
"id": 1569,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "eclipse",
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"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,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "hbase",
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 865,
"existing_alias_text": "Unit Testing",
"input_term": "Unit Testing",
"matched_canonical": {
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"display_name": "Unit Testing",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "unit-testing",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 193,
"existing_alias_text": "integration testing",
"input_term": "Integration Testing",
"matched_canonical": {
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"display_name": "Integration testing",
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"is_also_category": false,
"is_extractable": true,
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},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 864,
"existing_alias_text": "Code Review",
"input_term": "Code Review",
"matched_canonical": {
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"display_name": "Code Review",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PRACTICE",
"slug": "code-review",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 868,
"existing_alias_text": "Agile",
"input_term": "Agile",
"matched_canonical": {
"category_id": 8,
"display_name": "Agile",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 3594,
"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": 1851,
"existing_alias_text": "Scrum",
"input_term": "Scrum",
"matched_canonical": {
"category_id": 8,
"display_name": "Scrum",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "scrum",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
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"slug": "data-engineer",
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},
{
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},
{
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},
{
"display_name": "Android Developer",
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"role_archetype": null,
"slug": "android-engineer",
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},
{
"display_name": "Native Mobile Developer",
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"role_archetype": "Engineering",
"slug": "native-mobile-developer",
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},
{
"display_name": "Pega Developer",
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"slug": "pega-developer",
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},
{
"display_name": "Backend Developer",
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"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
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},
{
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},
{
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"role_archetype": "Engineering",
"slug": "fullstack-developer",
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},
{
"display_name": "Engineering Manager",
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"slug": "engineering-manager",
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},
{
"display_name": "ML Engineer",
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"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"slug": "ml-ops-engineer",
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},
{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
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},
{
"display_name": ".NET Backend Developer",
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"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
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},
{
"display_name": "PHP Backend Developer",
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},
{
"display_name": "Python Backend Developer",
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}
],
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"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Hadoop/Spark-based data engineering, ETL-style data flows, warehousing support, and production data pipeline maintenance rather than pure BI or modeling work.",
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"slug": "data-engineer",
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},
"dimensions": [
{
"dimension": {
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"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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},
"input_skill": "Hadoop",
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{
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]
},
{
"dimension": {
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},
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]
},
{
"dimension": {
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},
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{
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{
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{
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}
]
},
{
"dimension": {
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"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.",
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},
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{
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]
},
{
"dimension": {
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"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.",
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},
"input_skill": "Java",
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"roles_from_db": [
{
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]
},
{
"dimension": {
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},
"input_skill": "Java",
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]
},
{
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]
},
{
"dimension": {
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},
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]
},
{
"dimension": {
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},
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]
},
{
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},
"input_skill": "Unix shell scripting",
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{
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{
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]
},
{
"dimension": {
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"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.",
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},
"input_skill": "Eclipse",
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},
{
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"alias_text": "Code Review",
"alias_type": "CANONICAL",
"id": 864,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 58,
"display_name": "Code Review",
"id": 516,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PRACTICE",
"slug": "code-review",
"sub_category_id": 364,
"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": "Code Review",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Code Review",
"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": "Design Review",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Soft Skills",
"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": "design-review",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Agile",
"alias_type": "CANONICAL",
"id": 868,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 3594,
"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": "Agile",
"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": "Agile",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "Agile",
"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": "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": "Iterative Development",
"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": "iterative-development",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Sqoop",
"Pig",
"HiveQL",
"UDF",
"MapReduce",
"Business Intelligence",
"Design Review",
"Iterative Development"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Data Engineer",
"id": 2,
"rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on Hadoop/Spark-based data engineering, ETL-style data flows, warehousing support, and production data pipeline maintenance rather than pure BI or modeling work.",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Spark",
"tag": "in_db"
},
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "Unix shell scripting",
"tag": "in_db"
},
{
"skill": "Sqoop",
"tag": "new"
},
{
"skill": "Eclipse",
"tag": "in_db"
},
{
"skill": "Pig",
"tag": "new"
},
{
"skill": "HiveQL",
"tag": "new"
},
{
"skill": "UDF",
"tag": "new"
},
{
"skill": "HBase",
"tag": "in_db"
},
{
"skill": "MapReduce",
"tag": "new"
},
{
"skill": "Business Intelligence",
"tag": "new"
},
{
"skill": "Unit Testing",
"tag": "in_db"
},
{
"skill": "Integration Testing",
"tag": "in_db"
},
{
"skill": "Code Review",
"tag": "in_db"
},
{
"skill": "Design Review",
"tag": "new"
},
{
"skill": "Agile",
"tag": "in_db"
},
{
"skill": "Scrum",
"tag": "in_db"
},
{
"skill": "Iterative Development",
"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": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Hadoop",
"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": 1351,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Spark",
"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": 1350,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"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"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-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 \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"input_skill": "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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"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,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Unix shell scripting",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"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"
},
"dimension_id": 96,
"input_skill": "Eclipse",
"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": [],
"skill_dimension_saved": true,
"skill_id": 1569,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "HBase",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1352,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"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"
},
"dimension_id": 96,
"input_skill": "Unit Testing",
"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": [],
"skill_dimension_saved": true,
"skill_id": 517,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Testing and Defect Resolution",
"id": 262,
"rationale": "Validates Pega rules, flows, and integrations and then troubleshoots defects found in lower environments or production. This is a coherent cluster because the role is expected to verify platform behavior and fix rule-level issues.",
"slug": "testing-and-defect-resolution",
"source": "db"
},
"dimension_id": 262,
"input_skill": "Integration Testing",
"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": 56,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Testing and Quality Assurance",
"id": 12,
"rationale": "Backend-specific test strategies used to validate service behavior and integration points. Covers automated test layers, contract checks, fixtures, and regression prevention.",
"slug": "testing-and-quality-assurance",
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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.
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