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Pipeline run

15ef69d1-4173-4031-823a-7fa5484be674

Pipeline LLM cost (USD)
API 1: $0.0073 API 2: $0.0001 API 3: $0.0000 Total: $0.0074

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: no_kras · tech_stack_maturity: jd
Nature of work no kras
Vague JD — no KRAs present to derive a specific nature of work.
Tech stack maturity
Mainstream Modern
The stack centers on widely adopted big-data and distributed data processing technologies like Kafka, Spark, Hadoop, and Java/Scala/Python, which are common in modern enterprise data engineering but not specifically cloud-native or bleeding-edge.
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 (10)
Spark Scala Java MongoDB Azure Databricks Hadoop PySpark Python Apache Kafka Kafka Streaming
Skill cluster (4 dimension groups, role-scoped)
Programming Languages for Data Work
Scala Java Python
ETL and ELT Tooling
Spark Hadoop
Messaging and Event Streaming
Apache Kafka
Cross-cutting / unaligned
MongoDB Azure Databricks PySpark Kafka Streaming

Signals

Skill data-engineer
0.60
Alias —
—
KRA data-engineer
0.46

Post-classification

Centroidupdated · n=385
Alias collision log—
New-role queue—
New skills captured3
New KRA captured—

Captured for admin review

Azure Databricks primary ↔ Data Engineer pending
PySpark primary ↔ Data Engineer pending
Kafka Streaming primary ↔ Data Engineer pending
Status: completed Created: 2026-05-27T16:05:25.006018Z Updated: 2026-05-27T16:06:40.263264Z API 3 duration: 49921 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 explicitly for a Data Engineer and emphasizes big data, Spark, Databricks, Kafka streaming, and related engineering work.

Matched skills

SparkScalaJavaMongoDBAzure DatabricksHadoopPySparkPythonApache KafkaKafka Streaming

Matched dimensions

Data EngineeringBig Data ProcessingCloud Data EngineeringStreaming Data IntegrationETL/Data Pipeline Development

Matched KRAs

Experience with Spark, Scala, Java, MongoDBExperience with Azure DatabricksHands-on experience in Hadoop, Spark or PySparkStreaming: Kafka Streaming

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
2
Skipped

Job description

Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto “ Building Success Together® ,” Nisum has grown to over 1,800 professionals across the United States, Chile,Colombia, India, Pakistan and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today’s world, with immersive and seamless experiences across digital and physical channels.


What You Know
o 7-10 years of experience in a Data Engineer role
o Experience with Spark, Scala, Java, MongoDB
o Experience with Azure Databricks
o Hands-on experience in Hadoop, Spark or PySpark, Java/Scala/Python, Azure Databricks, MongoDB, Apache Kafka
o Streaming: Kafka Streaming. 


Education
• Bachelor’s / Master’s degree in specific technical fields like computer science, math, statistics preferred; or equivalent practical experience
Benefits
• In addition to competitive salaries and benefits packages, Nisum India offers its employees some unique and fun extras:
• Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis. We support our team to excel in their field.
• Parental Medical Insurance - Nisum believes our team is the heart of our business and we want to make sure to take care of the heart of theirs. We offer opt-in parental medical insurance in addition to our medical benefits.
• Activities -From the Nisum Premier League's cricket tournaments to hosted Hack-a-thon, Nisum employees can participate in a variety of team building activities such as skits, dances performance in addition to festival celebrations.
• Free Meals - Free snacks and dinner is provided on a daily basis, in addition to subsidized lunch.
Nisum is an Equal Opportunity Employer and we are proud of our ongoing efforts to foster diversity and inclusion in the workplace.

Skills from this JD

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

Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Scala Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Scala id=102 · scala

Aliases — catalog

  • Scala (CANONICAL) primary

Context tags (catalog)

Akka Apache Kafka Cats Flink JVM Monads Play Framework SBT ScalaTest Shapeless Spark Spark SQL ZIO case class for-comprehension functional programming implicit pattern matching typeclass

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
EPFL
License
apache_2
Year introduced
2004
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Data Work
programming-languages-for-data-work
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
MongoDB Primary 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)
Azure Databricks 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
Cloud Platforms
Sub-category
Data Engineering Tools
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Hadoop Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hadoop id=1351 · hadoop

Aliases — catalog

  • Hadoop (CANONICAL)

Context tags (catalog)

Big Data Data Lake Distributed Computing ELT ETL Flume HDFS Hive Kafka MapReduce NoSQL Oozie Pig Spark Sqoop YARN

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
91
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
PySpark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
 — — Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka Primary 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
Kafka Streaming Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka Streams id=122 · kafka-streams

Aliases — catalog

  • Kafka Streams (CANONICAL) primary

Context tags (catalog)

GlobalKTable KStream KTable Processor API SerDes aggregations event-driven architecture exactly-once semantics interactive queries joins repartitioning state store stream-table join topology windowing

Stored enrichment (catalog DB)

Category
Library
Sub-category
Stream Processing Library
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2016
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in JVM streaming JDs and Kafka ecosystem roles; widely used for stateful stream processing alongside Apache Kafka, with steady GitHub activity and no sunset signal.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
99
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Stream Processing Systems Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Stream Processing Systems
stream-processing-systems
 — — Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed

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
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
MongoDB in_db
NoSQL Databases
nosql-databases
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
PySpark new
ETL and ELT Tooling
etl-and-elt-tooling
 — — Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
 ✓ — Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
 ✓ ✓ Existing dimension (library) · Role↔dimension saved
Kafka Streaming new
Stream Processing Systems
stream-processing-systems
 — — Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Azure Databricks | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR
dimension_skill_link_proposed PySpark ↔ ETL and ELT Tooling
role_dimension_link_proposed Data Engineer ↔ ETL and ELT Tooling
dimension_skill_link_proposed Kafka Streaming ↔ Stream Processing Systems
role_dimension_link_proposed Data Engineer ↔ Stream Processing Systems
nano JD Parser — gpt-4.1-nano click to toggle
JD type fail
Show raw JSON 
{
  "JD_type": "fail",
  "archetype_override_applied": true,
  "archetype_override_matched_skills": [
    "Python",
    "MongoDB",
    "Scala",
    "Apache Kafka",
    "Make",
    "Databricks",
    "Analytics",
    "Hadoop",
    "Azure",
    "Java",
    "sessions",
    "Role",
    "channels",
    "Kafka"
  ],
  "role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle 
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "Java"
    },
    {
      "is_primary": true,
      "skill_name": "MongoDB"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "Kafka Streaming"
    }
  ],
  "jd_role": null,
  "nano_parsed": {
    "JD_type": "fail",
    "archetype_override_applied": true,
    "archetype_override_matched_skills": [
      "Python",
      "MongoDB",
      "Scala",
      "Apache Kafka",
      "Make",
      "Databricks",
      "Analytics",
      "Hadoop",
      "Azure",
      "Java",
      "sessions",
      "Role",
      "channels",
      "Kafka"
    ],
    "role_archetype": "Engineering"
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "15ef69d1-4173-4031-823a-7fa5484be674",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "o Hands-on experience in Hadoop, Spark or PySpark, Java/Scala/Python, Azure Databricks, MongoDB, Apache Kafka",
            "similarity": 0.6247
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "o 7-10 years of experience in a Data Engineer role",
            "similarity": 0.4348
          },
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Bachelor\u2019s / Master\u2019s degree in specific technical fields like computer science, math, statistics preferred; or equivalent practical experience",
            "similarity": 0.3158
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.4584,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": [
          {
            "kra_text": "coach performance and growth",
            "sentence": "Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis.",
            "similarity": 0.4309
          },
          {
            "kra_text": "coach performance and growth",
            "sentence": "We support our team to excel in their field.",
            "similarity": 0.425
          },
          {
            "kra_text": "facilitate technical and delivery decisions",
            "sentence": "A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today\u2019s world, with immersive and seamless experiences across digital and physical channels.",
            "similarity": 0.3489
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 121,
        "score": 0.4016,
        "slug": "engineering-manager",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "o Hands-on experience in Hadoop, Spark or PySpark, Java/Scala/Python, Azure Databricks, MongoDB, Apache Kafka",
            "similarity": 0.3955
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "o 7-10 years of experience in a Data Engineer role",
            "similarity": 0.3829
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis.",
            "similarity": 0.3137
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.364,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "o Hands-on experience in Hadoop, Spark or PySpark, Java/Scala/Python, Azure Databricks, MongoDB, Apache Kafka",
            "similarity": 0.4043
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "o 7-10 years of experience in a Data Engineer role",
            "similarity": 0.3371
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve direct business growth by building the advanced technology they need to reach end customers in today\u2019s world, with immersive and seamless experiences across digital and physical channels.",
            "similarity": 0.3166
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.3527,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
            "sentence": "o Hands-on experience in Hadoop, Spark or PySpark, Java/Scala/Python, Azure Databricks, MongoDB, Apache Kafka",
            "similarity": 0.3578
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Continuous Learning - Year-round training sessions are offered as part of skill enhancement certifications sponsored by the company on an as need basis.",
            "similarity": 0.3442
          },
          {
            "kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
            "sentence": "o 7-10 years of experience in a Data Engineer role",
            "similarity": 0.302
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.3347,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 6,
        "matched_skills": [
          "Apache Kafka",
          "Apache Spark",
          "Hadoop",
          "Java",
          "Python",
          "Scala"
        ],
        "role_id": 2,
        "score": 0.6,
        "slug": "data-engineer",
        "total_count": 10
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Apache Kafka",
          "Java",
          "MongoDB",
          "Python"
        ],
        "role_id": 1,
        "score": 0.4,
        "slug": "backend-engineer",
        "total_count": 10
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Java",
          "Python"
        ],
        "role_id": 15,
        "score": 0.2,
        "slug": "full-stack-engineer",
        "total_count": 10
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "Scala"
        ],
        "role_id": 3,
        "score": 0.2,
        "slug": "ml-engineer",
        "total_count": 10
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Python",
          "Scala"
        ],
        "role_id": 16,
        "score": 0.2,
        "slug": "ml-ops-engineer",
        "total_count": 10
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 0.98,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.98,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Data Engineering",
      "Big Data Processing",
      "Cloud Data Engineering",
      "Streaming Data Integration",
      "ETL/Data Pipeline Development"
    ],
    "matched_kras": [
      "Experience with Spark, Scala, Java, MongoDB",
      "Experience with Azure Databricks",
      "Hands-on experience in Hadoop, Spark or PySpark",
      "Streaming: Kafka Streaming"
    ],
    "matched_skills": [
      "Spark",
      "Scala",
      "Java",
      "MongoDB",
      "Azure Databricks",
      "Hadoop",
      "PySpark",
      "Python",
      "Apache Kafka",
      "Kafka Streaming"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is explicitly for a Data Engineer and emphasizes big data, Spark, Databricks, Kafka streaming, and related engineering work.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 385,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 18222,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Azure Databricks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18223,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "PySpark",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 18224,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Kafka Streaming",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details 
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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": 272,
      "existing_alias_text": "Scala",
      "input_term": "Scala",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Scala",
        "id": 102,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "scala",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1,
      "existing_alias_text": "Java",
      "input_term": "Java",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Java",
        "id": 1,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "java",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Hadoop",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Hadoop",
        "id": 1351,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "hadoop",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "PySpark",
      "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": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 326,
      "existing_alias_text": "Kafka Streams",
      "input_term": "Kafka Streaming",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "Kafka Streams",
        "id": 122,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "kafka-streams",
        "sub_category_id": 99,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "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"
    },
    {
      "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": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    }
  ],
  "chosen_role": {
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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.",
            "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": [
        {
          "alias_text": "Kafka Streams",
          "alias_type": "CANONICAL",
          "id": 326,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "Kafka Streams",
        "id": 122,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "kafka-streams",
        "sub_category_id": 99,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Stream Processing Systems",
            "id": 25,
            "rationale": "Technologies for processing event streams and near-real-time data flows. This includes stream transformations, windowing, stateful processing, and stream-to-warehouse delivery patterns.",
            "slug": "stream-processing-systems",
            "source": "db"
          },
          "input_skill": "Kafka Streaming",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Kafka Streaming",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Azure Databricks"
  ]
}
API 3 — final-role-output 
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is explicitly for a Data Engineer and emphasizes big data, Spark, Databricks, Kafka streaming, and related engineering work.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "MongoDB",
      "tag": "in_db"
    },
    {
      "skill": "Azure Databricks",
      "tag": "new"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "PySpark",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Apache Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Kafka Streaming",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Scala",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 102,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Scala",
        "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": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 102,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Java Language and JVM",
          "id": 279,
          "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
          "slug": "java-language-and-jvm",
          "source": "db"
        },
        "dimension_id": 279,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Kotlin and Java",
          "id": 161,
          "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
          "slug": "kotlin-and-java",
          "source": "db"
        },
        "dimension_id": 161,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Android Developer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "android-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Native Mobile Languages",
          "id": 274,
          "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
          "slug": "native-mobile-languages",
          "source": "db"
        },
        "dimension_id": 274,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL Databases",
          "id": 19,
          "rationale": "Models and manages data using non-relational database systems.",
          "slug": "nosql-databases",
          "source": "db"
        },
        "dimension_id": 19,
        "input_skill": "MongoDB",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 91,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Hadoop",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1351,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "PySpark",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "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": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
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