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Pipeline run

26a5dd1d-ddb6-49ce-b8db-c77c2abd8359

Pipeline LLM cost (USD)
API 1: $0.0101 API 2: $0.0003 API 3: $0.0000 Total: $0.0104

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and tune Java-based ETL/data pipelines, support warehouses/lakes and SQL-backed data flows, and work with analysts and engineers to fix data issues, validate quality, and improve reliability/performance.
""Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies.""
Tech stack maturity
Mainstream Modern
Java and SQL are widely used, established data engineering technologies that fit a modern but not bleeding-edge stack.
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 (27)
Java ETL SQL Apache Hadoop Spark Flink MySQL PostgreSQL Oracle MongoDB Cassandra Redis AWS Azure Google Cloud Platform AWS Glue Azure Data Factory BigQuery Tableau Power BI Looker Docker Kubernetes Apache Kafka Apache Storm +2
Skill cluster (12 dimension groups, role-scoped)
BI and Visualization Tools
Tableau Power BI Looker
Cloud Platforms
AWS Azure
ETL and ELT Tooling
Spark Flink
Programming Languages for Data Work
Java SQL
Relational Database Usage
MySQL PostgreSQL
Caching and State Management
Redis
Cloud Data Warehouses
BigQuery
Cloud Provider Platforms
Google Cloud Platform
Container Orchestration Platforms
Kubernetes
Containerization and Image Builds
Docker
Messaging and Event Streaming
Apache Kafka
Cross-cutting / unaligned
ETL Apache Hadoop Oracle MongoDB Cassandra AWS Glue Azure Data Factory Apache Storm Scrum Kanban
Show KRA description ↓
1. Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies. 2. Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions. 3. Optimise and fine-tune existing data pipelines for improved performance and reliability. 4. Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems. 5. Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies. 6. Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations. 7. Stay up-to-date with emerging technologies and trends in data engineering and incorporate them into our data infrastructure and processes. 8. Document design specifications, technical solutions, and best practices for knowledge sharing and future reference. 1. Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 2. 3 to 6 years of professional experience in software development with a focus on data engineering. 3. Strong proficiency in Java programming language, with hands-on experience in building scalable applications and data pipelines. 4. Experience with distributed computing frameworks such as Apache Hadoop, Spark, or Flink. 5. Proficiency in SQL and experience with relational databases like MySQL, PostgreSQL, or Oracle. 6. Experience with NoSQL databases such as MongoDB, Cassandra, or Redis is a plus. 7. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, and experience with related services like AWS Glue, Azure Data Factory, or Google BigQuery. 8. Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and implement effective solutions. 9. Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment. 10. Proven ability to manage multiple priorities and deliver results in a fast-paced and dynamic environment. 1. Experience with data visualisation tools such as Tableau, Power BI, or Looker. 2. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes. 3. Knowledge of stream processing frameworks such as Apache Kafka or Apache Storm. 4. Experience with agile software development methodologies like Scrum or Kanban.

Signals

Skill engineering-manager
0.67
Alias data-engineer
1.00
KRA data-engineer
0.69

Post-classification

Centroidupdated · n=428
Alias collision log
New-role queue
New skills captured7
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
Apache Hadoop Data Engineer pending
Oracle Data Engineer pending
AWS Glue Data Engineer pending
Azure Data Factory Data Engineer pending
Apache Storm Data Engineer pending
Kanban Data Engineer pending
Status: completed Created: 2026-05-27T16:23:30.878926Z Updated: 2026-05-27T16:26:29.474586Z API 3 duration: 125688 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 DOMAIN

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

Domain=Data Engineering & Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.

Matched skills

Javadata pipelinesETLApache HadoopSparkFlinkSQLMySQLPostgreSQLOracleMongoDBCassandraRedisAWSAzure

Matched dimensions

Data Pipeline EngineeringETL DevelopmentData Infrastructure EngineeringPipeline Performance OptimizationData Quality and MonitoringCloud Data EngineeringCross-functional CollaborationTroubleshooting and Reliability

Matched KRAs

Design, develop, and maintain scalable and efficient data pipelinesCollaborate with cross-functional teamsOptimise and fine-tune existing data pipelinesBuild and maintain data infrastructureEnsure data quality and integrityTroubleshoot data-related issuesDocument design specifications, technical solutions, and best practices

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

Job Title: Data Engineer (Java Backend)


Location: Bangalore


Experience: 3 to 6 years


Employment Type: Full Time


Job Description: 
We are seeking a talented and experienced Data Engineer proficient in Java to join our team in Bangalore. As a Data Engineer, you will be responsible for designing, developing, and maintaining data pipelines, ETL processes, and data infrastructure to support our organisation's data needs. The ideal candidate should have a strong background in Java development with a focus on data engineering, along with excellent problem-solving skills and a passion for working with large datasets.
Responsibilities: 
1. Design, develop, and maintain scalable and efficient data pipelines and ETL processes using Java technologies.
2. Collaborate with cross-functional teams including data scientists, analysts, and software engineers to understand data requirements and design appropriate solutions.
3. Optimise and fine-tune existing data pipelines for improved performance and reliability.
4. Build and maintain data infrastructure, including data warehouses, data lakes, and data processing systems.
5. Ensure data quality and integrity throughout the data pipeline by implementing effective data validation and monitoring strategies.
6. Troubleshoot data-related issues and implement timely resolutions to minimise impact on business operations.
7. Stay up-to-date with emerging technologies and trends in data engineering and incorporate them into our data infrastructure and processes.
8. Document design specifications, technical solutions, and best practices for knowledge sharing and future reference.
Requirements: 
1. Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
2. 3 to 6 years of professional experience in software development with a focus on data engineering.
3. Strong proficiency in Java programming language, with hands-on experience in building scalable applications and data pipelines.
4. Experience with distributed computing frameworks such as Apache Hadoop, Spark, or Flink.
5. Proficiency in SQL and experience with relational databases like MySQL, PostgreSQL, or Oracle.
6. Experience with NoSQL databases such as MongoDB, Cassandra, or Redis is a plus.
7. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform, and experience with related services like AWS Glue, Azure Data Factory, or Google BigQuery.
8. Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues and implement effective solutions.
9. Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
10. Proven ability to manage multiple priorities and deliver results in a fast-paced and dynamic environment.
Preferred Qualifications: 
1. Experience with data visualisation tools such as Tableau, Power BI, or Looker.
2. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.
3. Knowledge of stream processing frameworks such as Apache Kafka or Apache Storm.
4. Experience with agile software development methodologies like Scrum or Kanban.
Join us in building innovative data solutions that drive business growth and unlock insights from large-scale data sets. If you have a passion for data engineering and thrive in a collaborative environment, we'd love to hear from you!

Skills from this JD

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Apache Hadoop Secondary 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Spark Secondary 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
Flink Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Flink id=1349 · flink

Aliases — catalog

  • Apache Flink (VERSION)
  • Flink (CANONICAL)
  • Flink 1.20 (VERSION)
  • Flink 1.20.x (VERSION)
  • Flink 1.x (VERSION)

Context tags (catalog)

Apache Beam Flink SQL Kafka backpressure checkpointing data pipeline dataflow event time flink-connector flink-ml flink-runtime real-time analytics stateful processing streaming windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
1.20

Maturity reasoning: Apache Flink appears in many data/streaming job postings and is a standard choice alongside Kafka/Spark for real-time ETL; its GitHub and vendor ecosystem remain active, indicating broad adoption.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
94
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
MySQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MySQL id=17 · mysql

Aliases — catalog

  • MySQL (CANONICAL) primary

Context tags (catalog)

ACID ACID compliance Backup Data Warehousing Database Design ER Diagrams EXPLAIN Indexes InnoDB JOIN Joins MyISAM Normalization PHPMyAdmin Query Optimization Replication SQL SQL queries Stored Procedures Transactions Triggers backup backup and restore backup strategies data modeling data normalization database design database normalization database schema foreign keys indexes indexing joins master-slave normalization performance tuning query optimization read replicas replication schema design schema management sharding stored procedures transaction isolation transaction management transactions triggers

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Oracle Corporation
License
gpl_v2
Year introduced
1995
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: MySQL appears in a large share of backend/DB job descriptions and remains a standard managed offering across AWS RDS, Cloud SQL, and Azure Database, indicating broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Oracle 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
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
MongoDB Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MongoDB id=91 · mongodb

Aliases — catalog

  • MongoDB (CANONICAL) primary
  • MongoDB 2.0 (VERSION)
  • MongoDB 2.2 (VERSION)
  • MongoDB 2.4 (VERSION)
  • MongoDB 2.6 (VERSION)
  • MongoDB 3.0 (VERSION)
  • MongoDB 3.2 (VERSION)
  • MongoDB 3.4 (VERSION)
  • MongoDB 3.6 (VERSION)
  • MongoDB 4 (VERSION)
  • MongoDB 4.0 (VERSION)
  • MongoDB 4.2 (VERSION)
  • MongoDB 4.4 (VERSION)
  • MongoDB 5 (VERSION)
  • MongoDB 5.0 (VERSION)
  • MongoDB 6 (VERSION)
  • MongoDB 6.0 (VERSION)
  • MongoDB 7 (VERSION)
  • MongoDB 7.0 (VERSION)
  • MongoDB 8 (VERSION)
  • MongoDB 8.0 (VERSION)

Context tags (catalog)

BSON CRUD GridFS MongoDB Atlas Mongoose NoSQL TTL index aggregation pipeline change streams collections documents indexes replica set sharding

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Document Database
Vendor
MongoDB, Inc.
License
other_open
Year introduced
2009
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
8.0

Maturity reasoning: MongoDB appears in many job descriptions across backend/data roles and is a standard document database in modern stacks; strong GitHub/community activity and broad cloud vendor support indicate mainstream adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
27
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cassandra id=1354 · cassandra

Aliases — catalog

  • Cassandra (CANONICAL)

Context tags (catalog)

CQL Cluster Consistency DSE Data Modeling DataStax Eventual Consistency NoSQL Partitioning Replication ScyllaDB Spark Thrift TinkerPop Wide Column

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Wide Column Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2008
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Apache Cassandra appears in many production data-platform JDs and is a common choice for high-write, distributed workloads; GitHub and vendor docs show sustained activity rather than sunset signals.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
31
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Redis Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Redis id=31 · redis

Aliases — catalog

  • Redis (CANONICAL) primary

Context tags (catalog)

JSON serialization JSON support LRU eviction Lua scripting Redis Cluster Redis Sentinel Redis Streams TTL backup and restore cache connection pooling data persistence hashes in-memory key expiration lists performance tuning persistence pipeline pub/sub replication sentinel sharding sorted sets streams transactions

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Key Value Store
Vendor
Redis Labs
License
apache_2
Year introduced
2009
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Redis appears in many job descriptions for caching, queues, and session storage, and is a standard datastore in modern backend stacks; vendor activity and broad ecosystem support indicate strong market demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
28
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Caching and State Management Catalog dimension db id 7

    Library dimension (catalog)

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

  • Magento Caching and Performance Catalog dimension db id 404

    Library dimension (catalog)

    Roles linked in library: Magento Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Caching and State Management
caching-and-state-management
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Magento Caching and Performance
magento-caching-and-performance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure id=188 · azure

Aliases — catalog

  • Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure AD Azure Active Directory Azure App Service Azure Blob Azure Blob Storage Azure Cognitive Services Azure Cosmos DB Azure DevOps Azure DevTest Labs Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Networking Azure Resource Manager Azure SQL Azure SQL Database Azure Security Center Azure Storage Azure Storage Explorer Azure Virtual Machines Bicep Blob Storage Cloud Services Cosmos DB Entra ID Functions Infrastructure as Code Key Vault Log Analytics Logic Apps Resource Groups Serverless Computing Service Bus Storage Account Terraform Virtual Machines

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
proprietary
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms & Managed Services Catalog dimension db id 221

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Google Cloud Platform id=425 · google-cloud-platform

Aliases — catalog

  • Google Cloud Platform (CANONICAL) primary

Context tags (catalog)

Anthos App Engine Artifact Registry BigQuery Cloud Build Cloud Functions Cloud Monitoring Cloud Pub/Sub Cloud Run Cloud SQL Cloud Spanner Cloud Storage Compute Engine Dataflow Dataproc GCP GKE IAM Kubernetes Kubernetes Engine Pub/Sub Serverless Stackdriver Terraform VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Google
License
other_open
Year introduced
2008
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GCP appears in many cloud-engineering job descriptions alongside AWS/Azure, and Google continues expanding managed services and certifications, indicating broad hiring demand rather than niche use.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

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

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Glue 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Data Factory 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
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
BigQuery Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: BigQuery id=106 · bigquery

Aliases — catalog

  • BigQuery (CANONICAL) primary

Context tags (catalog)

Cloud Storage Dataflow ELT ETL GCP Google Cloud Platform Looker Pub/Sub SQL Standard SQL clustered tables data warehouse dbt partitioned tables service account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Google
License
proprietary
Year introduced
2011
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Tableau Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Tableau id=150 · tableau

Aliases — catalog

  • Tableau (CANONICAL) primary

Context tags (catalog)

LOD expressions Tableau Cloud Tableau Desktop Tableau Prep Tableau Server actions calculated fields dashboards data blending data visualization extracts filters parameters published data sources workbooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Tableau Software
License
proprietary
Year introduced
2003
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Power BI Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Power BI id=151 · power-bi

Aliases — catalog

  • Power BI (CANONICAL) primary

Context tags (catalog)

Azure Synapse DAX DirectQuery Import mode M language Power Query RLS SQL Server SSAS dashboard data modeling data warehouse gateway reporting star schema

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Microsoft
License
proprietary
Year introduced
2015
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Power BI appears frequently in BI/data analyst job descriptions and is a standard Microsoft analytics platform in enterprise stacks, with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Looker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Looker id=152 · looker

Aliases — catalog

  • Looker (CANONICAL) primary

Context tags (catalog)

BigQuery Dimensions Explores LookML Measures PDT SQL Runner Snowflake dashboards data modeling derived table drill-down embedded analytics scheduled delivery tiles

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Google Cloud
License
proprietary
Year introduced
2012
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Looker appears frequently in BI/analytics job descriptions and is a standard enterprise analytics platform, especially after Google Cloud’s acquisition expanded market visibility.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Docker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=61 · docker

Aliases — catalog

  • Docker (CANONICAL) primary

Context tags (catalog)

CI/CD Compose DevOps Docker Compose Docker Swarm Dockerfile Kubernetes build pipeline container container lifecycle container orchestration container registry container security containerization containers image image registry images immutable infrastructure lightweight virtualization microservices networking orchestration port mapping registry scalability service discovery swarm volume volume management

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Containerization Tool
Vendor
Docker, Inc.
License
apache_2
Year introduced
2013
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
63
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Containerization and Image Builds Catalog dimension db id 152

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Deployment and Cloud Platforms Catalog dimension db id 418

    Library dimension (catalog)

    Roles linked in library: Ruby Backend Developer

  • Deployment and Runtime Configuration Catalog dimension db id 13

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Runtime Configuration
deployment-and-runtime-configuration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=726 · kubernetes

Aliases — catalog

  • Kubernetes (CANONICAL) primary
  • Kubernetes 1.0+ (VERSION)
  • Kubernetes 1.x (VERSION)
  • Kubernetes v1 (VERSION)
  • k8s (VERSION)
  • kubernetes 1.x (VERSION)
  • kubernetes latest (VERSION)

Context tags (catalog)

CI/CD Cluster Autoscaler ConfigMap DaemonSet Deployment Docker Grafana Helm Ingress Istio K8s Kubelet Namespace Pod Prometheus RBAC Secret Service StatefulSet containerization deployment etcd kubectl load balancing microservices namespace orchestration persistent storage scalability service mesh

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Container Orchestration Platform
Vendor
Cloud Native Computing Foundation
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
1.30

Maturity reasoning: Broadly adopted in cloud-native stacks; Kubernetes appears in a large share of DevOps/SRE job descriptions and is the default orchestration platform across major cloud vendors.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
557
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Container Orchestration Platforms Catalog dimension db id 134

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

  • Kubernetes for ML Workloads Catalog dimension db id 47

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Kafka id=145 · apache-kafka

Aliases — catalog

  • Apache Kafka (CANONICAL) primary

Context tags (catalog)

Avro Kafka Streams Schema Registry ZooKeeper brokers consumer group event streaming exactly-once semantics ksqlDB message queue offsets partitioning pub/sub replication topics

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Event Streaming Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Apache Kafka is broadly adopted in production and appears frequently in job descriptions for event streaming, data pipelines, and microservices; it remains a common hiring-pipeline staple across backend and platform roles.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
128
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Apache Storm 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Scrum Secondary 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)
Kanban 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
Java in_db
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Apache Hadoop new
ETL and ELT Tooling
etl-and-elt-tooling
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Flink in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Flink in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
MySQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MongoDB in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Redis in_db
Caching and State Management
caching-and-state-management
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Redis in_db
Magento Caching and Performance
magento-caching-and-performance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Azure in_db
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Platform in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Power BI in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Looker in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension saved
Docker in_db
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Runtime Configuration
deployment-and-runtime-configuration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Container Orchestration Platforms
container-orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Kubernetes for ML Workloads
kubernetes-for-ml-workloads
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Scrum in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scrum in_db
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed AWS Glue | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Azure Data Factory | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Apache Storm | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Kanban | type=Practices subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Apache Hadoop ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer (Java Backend)
Experience3 to 6 years
DomainIT Services & Consulting
Location Bangalore, India
JD type pass
Show raw JSON
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      "source_marker": {
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        "last_5_words": "or agile software development methodologies"
      },
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API 1 — extract-from-jd click to toggle
{
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    "matched_skills": [
      "Java",
      "data pipelines",
      "ETL",
      "Apache Hadoop",
      "Spark",
      "Flink",
      "SQL",
      "MySQL",
      "PostgreSQL",
      "Oracle",
      "MongoDB",
      "Cassandra",
      "Redis",
      "AWS",
      "Azure"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 428,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 19762,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19764,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Apache Hadoop",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19766,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Oracle",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19768,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "AWS Glue",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19769,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Data Factory",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19770,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Apache Storm",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 19771,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Kanban",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1,
      "existing_alias_text": "Java",
      "input_term": "Java",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Java",
        "id": 1,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "java",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Apache Hadoop",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Hadoop",
        "id": 1351,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "hadoop",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1999,
      "existing_alias_text": "Flink",
      "input_term": "Flink",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Flink",
        "id": 1349,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "flink",
        "sub_category_id": 94,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
      "input_term": "SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "SQL",
        "id": 101,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 134,
      "existing_alias_text": "MySQL",
      "input_term": "MySQL",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "MySQL",
        "id": 17,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "mysql",
        "sub_category_id": 29,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 121,
      "existing_alias_text": "PostgreSQL",
      "input_term": "PostgreSQL",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "PostgreSQL",
        "id": 16,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "postgresql",
        "sub_category_id": 29,
        "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": 232,
      "existing_alias_text": "MongoDB",
      "input_term": "MongoDB",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "MongoDB",
        "id": 91,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "mongodb",
        "sub_category_id": 27,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2013,
      "existing_alias_text": "Cassandra",
      "input_term": "Cassandra",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Cassandra",
        "id": 1354,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "cassandra",
        "sub_category_id": 31,
        "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": 168,
      "existing_alias_text": "Redis",
      "input_term": "Redis",
      "matched_canonical": {
        "category_id": 3,
        "display_name": "Redis",
        "id": 31,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "redis",
        "sub_category_id": 28,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 406,
      "existing_alias_text": "AWS",
      "input_term": "AWS",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "AWS",
        "id": 187,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 46,
        "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": 407,
      "existing_alias_text": "Azure",
      "input_term": "Azure",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure",
        "id": 188,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure",
        "sub_category_id": 46,
        "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": 741,
      "existing_alias_text": "Google Cloud Platform",
      "input_term": "Google Cloud Platform",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Google Cloud Platform",
        "id": 425,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "google-cloud-platform",
        "sub_category_id": 46,
        "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": 300,
      "existing_alias_text": "BigQuery",
      "input_term": "BigQuery",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "BigQuery",
        "id": 106,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "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": 359,
      "existing_alias_text": "Tableau",
      "input_term": "Tableau",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Tableau",
        "id": 150,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "tableau",
        "sub_category_id": 111,
        "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": 360,
      "existing_alias_text": "Power BI",
      "input_term": "Power BI",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Power BI",
        "id": 151,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "power-bi",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 361,
      "existing_alias_text": "Looker",
      "input_term": "Looker",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Looker",
        "id": 152,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "looker",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 198,
      "existing_alias_text": "Docker",
      "input_term": "Docker",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Docker",
        "id": 61,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "docker",
        "sub_category_id": 63,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1267,
      "existing_alias_text": "Kubernetes",
      "input_term": "Kubernetes",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Kubernetes",
        "id": 726,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kubernetes",
        "sub_category_id": 557,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 349,
      "existing_alias_text": "Apache Kafka",
      "input_term": "Apache Kafka",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Apache Kafka",
        "id": 145,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-kafka",
        "sub_category_id": 128,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "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",
        "id": 1215,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "scrum",
        "sub_category_id": 3627,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
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      "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"
    },
    {
      "display_name": "Android Developer",
      "id": 4,
      "rationale": null,
      "role_archetype": null,
      "slug": "android-engineer",
      "source": "db"
    },
    {
      "display_name": "Native Mobile Developer",
      "id": 75,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "native-mobile-developer",
      "source": "db"
    },
    {
      "display_name": "Pega Developer",
      "id": 24,
      "rationale": null,
      "role_archetype": null,
      "slug": "pega-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "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"
    },
    {
      "display_name": "Engineering Manager",
      "id": 121,
      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
      "source": "db"
    },
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": ".NET Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-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": "Python Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Go Backend Developer",
      "id": 81,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Cloud Architect",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
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    },
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      "display_name": "Magento Dev",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "magento-dev",
      "source": "db"
    },
    {
      "display_name": "Cyber Security Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "DevOps Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
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    },
    {
      "display_name": "ML Engineer",
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      "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"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    },
    {
      "display_name": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "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"
      },
      "input_skill": "Java",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Java Backend Developer",
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          "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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          "slug": "scala-backend-developer",
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    {
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        "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
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    {
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        "display_name": "Cloud Provider Platforms",
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        "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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        "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
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        "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
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    {
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        {
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        {
          "display_name": "Scala Backend Developer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platforms \u0026 Managed Services",
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        "rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
        "slug": "cloud-platforms-managed-services",
        "source": "db"
      },
      "input_skill": "Azure",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Fullstack Developer",
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          "role_archetype": null,
          "slug": "full-stack-engineer",
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        {
          "display_name": "Go Backend Developer",
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          "role_archetype": "Engineering",
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        {
          "display_name": "Node.js Backend Developer",
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          "role_archetype": "Engineering",
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          "source": "db"
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platforms for AI Deployment",
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        "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
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      "roles_from_db": [
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    {
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        "difficulty_hint": "well_known",
        "display_name": "Cloud Provider Platforms",
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        "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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        "source": "db"
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        {
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          "slug": "cloud-security-engineer",
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    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Posture Tools",
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        "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
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      "roles_from_db": [
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        {
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          "slug": "cybersecurity-engineer",
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    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "Vendor Product Families",
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        "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
        "slug": "vendor-product-families",
        "source": "db"
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      "input_skill": "Azure",
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      "roles_from_db": [
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          "slug": "engineering-manager",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud \u0026 Hosting Providers",
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        "rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
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        "source": "db"
      },
      "input_skill": "Google Cloud Platform",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "PHP Backend Developer",
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          "role_archetype": "Engineering",
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    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "Cloud Provider Platforms",
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        "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
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        "source": "db"
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        {
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          "slug": "cloud-security-engineer",
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    },
    {
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        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Posture Tools",
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    {
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        "difficulty_hint": "well_known",
        "display_name": "Vendor Product Families",
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        "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
        "slug": "vendor-product-families",
        "source": "db"
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      "input_skill": "Google Cloud Platform",
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    },
    {
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        "display_name": "Cloud Data Warehouses",
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        "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
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      "roles_from_db": [
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          "slug": "data-engineer",
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    {
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      "roles_from_db": [
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    {
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        "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
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        "rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
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      "roles_from_db": [
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        "source": "db"
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      "input_skill": "Docker",
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      "roles_from_db": [
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    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Deployment and Runtime Configuration",
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        "rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
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          "role_archetype": "Engineering",
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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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          "role_archetype": "Engineering",
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          "role_archetype": "Engineering",
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    {
      "dimension": {
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        "display_name": "Container Orchestration Platforms",
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        "rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
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        {
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    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "rationale": "Kubernetes-native components used to schedule, accelerate, and isolate ML training and serving workloads. This includes GPU enablement and ML-specific controllers rather than generic cluster administration.",
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      "input_skill": "Kubernetes",
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      "roles_from_db": [
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          "slug": "ml-engineer",
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        {
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          "slug": "ml-ops-engineer",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
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        "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
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      "input_skill": "Apache Kafka",
      "llm_role": null,
      "roles_from_db": [
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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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    {
      "aliases_in_db": [
        {
          "alias_text": "Looker",
          "alias_type": "CANONICAL",
          "id": 361,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Looker",
        "id": 152,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "looker",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "BI and Visualization Tools",
            "id": 31,
            "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
            "slug": "bi-and-visualization-tools",
            "source": "db"
          },
          "input_skill": "Looker",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Looker",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Docker",
          "alias_type": "CANONICAL",
          "id": 198,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Docker",
        "id": 61,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "docker",
        "sub_category_id": 63,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Containerization and Image Builds",
            "id": 152,
            "rationale": "Container image creation, tagging, hardening, and registry workflows used to package services for deployment. This is coherent because DevOps often owns the build-to-image path that feeds runtime environments.",
            "slug": "containerization-and-image-builds",
            "source": "db"
          },
          "input_skill": "Docker",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Deployment and Cloud Platforms",
            "id": 418,
            "rationale": "Platform-as-a-Service and container environments for deploying Ruby applications.",
            "slug": "deployment-and-cloud-platforms",
            "source": "db"
          },
          "input_skill": "Docker",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Deployment and Runtime Configuration",
            "id": 13,
            "rationale": "Configuration and release artifacts that control how backend services run in environments. Includes environment variables, manifests, feature flags, and release-safe configuration management.",
            "slug": "deployment-and-runtime-configuration",
            "source": "db"
          },
          "input_skill": "Docker",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Docker",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Kubernetes",
          "alias_type": "CANONICAL",
          "id": 1267,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.0+",
          "alias_type": "VERSION",
          "id": 1271,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.x",
          "alias_type": "VERSION",
          "id": 1270,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes v1",
          "alias_type": "VERSION",
          "id": 1269,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "k8s",
          "alias_type": "VERSION",
          "id": 1268,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "kubernetes 1.x",
          "alias_type": "VERSION",
          "id": 1400,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "kubernetes latest",
          "alias_type": "VERSION",
          "id": 1401,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Kubernetes",
        "id": 726,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kubernetes",
        "sub_category_id": 557,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Container Orchestration Platforms",
            "id": 134,
            "rationale": "Platforms that schedule and manage containerized workloads across clusters and environments. Cloud Architects need these to define workload placement standards, cluster boundaries, and platform capabilities.",
            "slug": "container-orchestration-platforms",
            "source": "db"
          },
          "input_skill": "Kubernetes",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Kubernetes for ML Workloads",
            "id": 47,
            "rationale": "Kubernetes-native components used to schedule, accelerate, and isolate ML training and serving workloads. This includes GPU enablement and ML-specific controllers rather than generic cluster administration.",
            "slug": "kubernetes-for-ml-workloads",
            "source": "db"
          },
          "input_skill": "Kubernetes",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Kubernetes",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Kafka",
          "alias_type": "CANONICAL",
          "id": 349,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Apache Kafka",
        "id": 145,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-kafka",
        "sub_category_id": 128,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Messaging and Event Streaming",
            "id": 8,
            "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
            "slug": "messaging-and-event-streaming",
            "source": "db"
          },
          "input_skill": "Apache Kafka",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Kafka",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Apache Storm",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "apache-storm",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Scrum",
          "alias_type": "CANONICAL",
          "id": 1851,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Scrum",
        "id": 1215,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "scrum",
        "sub_category_id": 3627,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Scrum",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Software Concepts, Patterns \u0026 Practices",
            "id": 478,
            "rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
            "slug": "software-concepts-patterns-practices",
            "source": "db"
          },
          "input_skill": "Scrum",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Scrum",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Kanban",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Practices",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "kanban",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Oracle",
    "AWS Glue",
    "Azure Data Factory",
    "Apache Storm",
    "Kanban"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building and optimizing scalable data pipelines, ETL, and data infrastructure with Java, Hadoop/Spark/Flink, SQL, and cloud services, which best matches a Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Apache Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Flink",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "MySQL",
      "tag": "in_db"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "MongoDB",
      "tag": "in_db"
    },
    {
      "skill": "Cassandra",
      "tag": "in_db"
    },
    {
      "skill": "Redis",
      "tag": "in_db"
    },
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "Azure",
      "tag": "in_db"
    },
    {
      "skill": "Google Cloud Platform",
      "tag": "in_db"
    },
    {
      "skill": "AWS Glue",
      "tag": "new"
    },
    {
      "skill": "Azure Data Factory",
      "tag": "new"
    },
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "Tableau",
      "tag": "in_db"
    },
    {
      "skill": "Power BI",
      "tag": "in_db"
    },
    {
      "skill": "Looker",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "Apache Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Apache Storm",
      "tag": "new"
    },
    {
      "skill": "Scrum",
      "tag": "in_db"
    },
    {
      "skill": "Kanban",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Java Language and JVM",
          "id": 279,
          "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
          "slug": "java-language-and-jvm",
          "source": "db"
        },
        "dimension_id": 279,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Kotlin and Java",
          "id": 161,
          "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
          "slug": "kotlin-and-java",
          "source": "db"
        },
        "dimension_id": 161,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Android Developer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "android-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Native Mobile Languages",
          "id": 274,
          "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
          "slug": "native-mobile-languages",
          "source": "db"
        },
        "dimension_id": 274,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
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