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Pipeline run

e13504ac-8d10-421c-aa7d-e7b997c4d80d

Pipeline LLM cost (USD)
API 1: $0.0079 API 2: $0.0004 API 3: $0.0000 Total: $0.0084

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD role baseline loaded sources · ai_index: role_baseline · 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 Legacy
The stack is centered on long-established enterprise data and Java ecosystem technologies such as Hadoop, Hive, HBase, Spark, Elasticsearch, and Jenkins, which are widely used but not typically considered modern cloud-native.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.20 / 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 (24)
Java J2EE Scala Spark Hadoop HDFS YARN Confluent Kafka HBase Hive Impala NoSQL Batch Processing AutoSys Cloudera SQL Elasticsearch ETL Sqoop Oracle Linux Git Jenkins CI/CD
Skill cluster (4 dimension groups, role-scoped)
Programming Languages for Data Work
Java Scala SQL
ETL and ELT Tooling
Spark Hadoop
Search and Content Discovery
Elasticsearch
Cross-cutting / unaligned
J2EE HDFS YARN Confluent Kafka HBase Hive Impala NoSQL Batch Processing AutoSys Cloudera ETL Sqoop Oracle Linux Git Jenkins CI/CD

Signals

Skill data-engineer
0.21
Alias
KRA data-engineer
0.63

Post-classification

Centroidupdated · n=160
Alias collision log
New-role queue
New skills captured11
New KRA captured

Captured for admin review

HDFS primary Data Engineer pending
YARN primary Data Engineer pending
Confluent Kafka primary Data Engineer pending
Impala primary Data Engineer pending
Batch Processing primary Data Engineer pending
AutoSys primary Data Engineer pending
Cloudera primary Data Engineer pending
ETL primary Data Engineer pending
Sqoop primary Data Engineer pending
Oracle primary Data Engineer pending
Linux primary Data Engineer pending
Status: completed Created: 2026-05-27T14:21:00.614086Z Updated: 2026-05-27T14:23:16.506324Z API 3 duration: 77625 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 large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches Data Engineer.

Matched skills

JavaJ2EEScalaSparkHadoopHDFSYARNConfluent KafkaHbaseHiveImpalaNoSQLAutoSysClouderaElasticsearchSqoopOracleLinuxGitJenkins CI/CD

Matched dimensions

Large-scale distributed data processingBig data platform engineeringBatch processing and job schedulingPerformance troubleshooting and cluster operationsHadoop ecosystem engineeringData security, management, and governance

Matched KRAs

Building large scale distributed data processing systems/applicationDevelop Big Data solution at enterprise scaleEnd to end implementationWork with Cloudera on open issuesImplement cluster configuration changes as neededPerformance analysis, troubleshooting and resolutionKnowledge of Hadoop Security, Data Management and Governance

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
0
Skipped

Job description

software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. • Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation • Strong experience in programming languages Java/J2EE/Scala. • Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. • Experience with Batch Processing and AutoSys Job Scheduling and Monitoring • Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) • Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed • Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc • Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD

Skills from this JD

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

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 for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
J2EE Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: J2EE id=1565 · j2ee

Aliases — catalog

  • J2EE (CANONICAL)

Context tags (catalog)

EJB GlassFish Hibernate JDBC JNDI JSF JSP Java EE Maven Microservices RESTful SOAP Servlets Spring WebLogic

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Enterprise Java Framework
Vendor
Oracle
License
other_open
Year introduced
1999
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: J2EE is largely superseded by Jakarta EE/Spring in modern JDs; Oracle’s Java EE/J2EE branding was replaced and current job postings rarely ask for J2EE specifically.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
DEPRECATED
Typical lifespan
SHORT_LIVED
Category id
5
Sub-category id
1172
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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)
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
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
HDFS Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
YARN Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Confluent Kafka 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
Message Brokers
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
HBase Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: HBase id=1352 · hbase

Aliases — catalog

  • HBase (CANONICAL)

Context tags (catalog)

Apache Bigtable Hadoop MapReduce NoSQL REST API Thrift column family data model data replication distributed real-time region server scalability table design

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Wide Column Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: HBase appears in a limited set of big-data/legacy Hadoop job postings, while newer JDs more often specify DynamoDB, Bigtable, or Cassandra; its market demand is specialized rather than broad.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hive Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hive id=2754 · hive

Aliases — catalog

  • Hive (CANONICAL) primary

Context tags (catalog)

Apache Apache Hive Bucketing ETL HQL Hive Metastore Hive SerDe HiveQL MapReduce SQL SQL-on-Hadoop big data bucketing columnar storage data lakes data warehousing integration metadata partitioning schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Local Key Value Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2242
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Local Persistence and Offline Behavior Catalog dimension db id 85

    Library dimension (catalog)

    Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Impala 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
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
NoSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=1346 · nosql

Aliases — catalog

  • NoSQL (CANONICAL)

Context tags (catalog)

CAP theorem Cassandra DynamoDB MongoDB Redis column-family data modeling document store eventual consistency graph database horizontal scaling key-value store query language schema-less sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Database Paradigm
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1019
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Batch Processing 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
AutoSys 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
Infrastructure Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Cloudera 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
Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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 for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Elasticsearch Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Elasticsearch id=3171 · elasticsearch

Aliases — catalog

  • Elasticsearch (CANONICAL) primary
  • ES (VERSION)
  • ElasticSearch (VERSION)
  • Elasticsearch 6 (VERSION)
  • Elasticsearch 6.x (VERSION)
  • Elasticsearch 7 (VERSION)
  • Elasticsearch 7.x (VERSION)
  • Elasticsearch 8 (VERSION)
  • Elasticsearch 8.x (VERSION)
  • Elasticsearch v6 (VERSION)
  • Elasticsearch v7 (VERSION)
  • Elasticsearch v8 (VERSION)

Context tags (catalog)

ELK stack JSON Kibana Logstash Lucene REST API RESTful API aggregation cluster management data visualization document store full-text search indexing performance tuning query DSL real-time search replication scalability search analytics search relevance sharding

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Search Datastore
Vendor
Elastic NV
License
apache_2
Year introduced
2010
Confidence
0.93
Version strategy
SEPARATE_ENTITY
Version tag
8.x

Maturity reasoning: Commonly listed in job descriptions for search/log analytics roles and widely deployed in production; Elastic’s docs and ecosystem show sustained adoption rather than sunset or replacement.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2925
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Magento Search and Indexing Catalog dimension db id 403

    Library dimension (catalog)

    Roles linked in library: Magento Dev

  • Search and Content Discovery Catalog dimension db id 356

    Library dimension (catalog)

    Roles linked in library: Drupal Dev, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
Sqoop Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Oracle 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
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Linux 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
Operating Systems
Sub-category
general
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Git Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Version Control Tool
Vendor
Linus Torvalds
License
gpl_v2
Year introduced
2005
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
730
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Jenkins id=283 · jenkins

Aliases — catalog

  • Jenkins (CANONICAL) primary

Context tags (catalog)

Blue Ocean CI/CD Declarative Pipeline Docker Groovy Jenkinsfile Kubernetes agents artifact repository artifacts automation build triggers integration multibranch pipeline pipeline plugins shared libraries stages test automation version control webhooks

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ci Cd Tool
Vendor
CloudBees
License
mit
Year introduced
2011
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Jenkins remains a common CI/CD requirement in job postings and enterprise DevOps stacks, with broad plugin ecosystem and long-running GitHub activity despite newer alternatives.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
184
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Ci Cd Process
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Java in_db
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
J2EE in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
HBase in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hive in_db
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Elasticsearch in_db
Magento Search and Indexing
magento-search-and-indexing
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch in_db
Search and Content Discovery
search-and-content-discovery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Git in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed HDFS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed YARN | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Confluent Kafka | type=Message Brokers subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Impala | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Batch Processing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed AutoSys | type=Infrastructure Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Cloudera | type=Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Sqoop | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Oracle | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Linux | type=Operating Systems subtype=general nature=TOOL lifespan=EVERGREEN
nano JD Parser — gpt-4.1-nano click to toggle
JD type fail
Show raw JSON
{
  "JD_type": "fail",
  "archetype_override_applied": true,
  "archetype_override_matched_skills": [
    "Scala",
    "J2EE",
    "Hive",
    "Jenkins",
    "Monitoring",
    "Git",
    "CI/CD",
    "NoSQL",
    "Hadoop",
    "HBase",
    "Java",
    "SQL",
    "Elasticsearch",
    "Kafka"
  ],
  "role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Java"
    },
    {
      "is_primary": true,
      "skill_name": "J2EE"
    },
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": true,
      "skill_name": "HDFS"
    },
    {
      "is_primary": true,
      "skill_name": "YARN"
    },
    {
      "is_primary": true,
      "skill_name": "Confluent Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "HBase"
    },
    {
      "is_primary": true,
      "skill_name": "Hive"
    },
    {
      "is_primary": true,
      "skill_name": "Impala"
    },
    {
      "is_primary": true,
      "skill_name": "NoSQL"
    },
    {
      "is_primary": true,
      "skill_name": "Batch Processing"
    },
    {
      "is_primary": true,
      "skill_name": "AutoSys"
    },
    {
      "is_primary": true,
      "skill_name": "Cloudera"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "Elasticsearch"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Sqoop"
    },
    {
      "is_primary": true,
      "skill_name": "Oracle"
    },
    {
      "is_primary": true,
      "skill_name": "Linux"
    },
    {
      "is_primary": true,
      "skill_name": "Git"
    },
    {
      "is_primary": true,
      "skill_name": "Jenkins"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    }
  ],
  "jd_role": null,
  "nano_parsed": {
    "JD_type": "fail",
    "archetype_override_applied": true,
    "archetype_override_matched_skills": [
      "Scala",
      "J2EE",
      "Hive",
      "Jenkins",
      "Monitoring",
      "Git",
      "CI/CD",
      "NoSQL",
      "Hadoop",
      "HBase",
      "Java",
      "SQL",
      "Elasticsearch",
      "Kafka"
    ],
    "role_archetype": "Engineering"
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "e13504ac-8d10-421c-aa7d-e7b997c4d80d",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
            "similarity": 0.633
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.633,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
            "similarity": 0.4131
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4131,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
            "similarity": 0.4052
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4052,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": [
          {
            "kra_text": "Designs and implements security controls including SIEM integration, endpoint detection and response, identity management, and firewall rule management.",
            "sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
            "similarity": 0.3907
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 5,
        "score": 0.3907,
        "slug": "cybersecurity-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "kra_matches": [
          {
            "kra_text": "Designs multi-region and multi-availability-zone cloud infrastructure architectures for high availability, fault tolerance, and horizontal scalability.",
            "sentence": "software development experience building large scale distributed data processing systems/application, Data Engineering or large scale internet systems. \u2022 Experience of at least 4 years in Developing/ Leading Big Data solution at enterprise scale with at least one end to end implementation \u2022 Strong experience in programming languages Java/J2EE/Scala. \u2022 Good experience in Spark/Hadoop/HDFS Architecture, YARN, Confluent Kafka , Hbase, Hive, Impala and NoSQL database. \u2022 Experience with Batch Processing and AutoSys Job Scheduling and Monitoring \u2022 Performance analysis, troubleshooting and resolution (this includes familiarity and investigation of Cloudera/Hadoop logs) \u2022 Work with Cloudera on open issues that would result in cluster configuration changes and then implement as needed \u2022 Strong experience with databases such as SQL,Hive, Elasticsearch, HBase, etc \u2022 Knowledge of Hadoop Security, Data Management and Governance Primary Skills: Java/Scala, ETL, Spark, Hadoop, Hive, Impala, Sqoop, HBase, Confluent Kafka, Oracle, Linux, Git, Jenkins CI/CD",
            "similarity": 0.384
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 9,
        "score": 0.384,
        "slug": "cloud-architect",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 5,
        "matched_skills": [
          "Apache Spark",
          "Hadoop",
          "Java",
          "SQL",
          "Scala"
        ],
        "role_id": 2,
        "score": 0.2083,
        "slug": "data-engineer",
        "total_count": 24
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "CI/CD",
          "Jenkins",
          "Scala"
        ],
        "role_id": 3,
        "score": 0.125,
        "slug": "ml-engineer",
        "total_count": 24
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "J2EE",
          "Java",
          "NoSQL"
        ],
        "role_id": 1,
        "score": 0.125,
        "slug": "backend-engineer",
        "total_count": 24
      },
      {
        "display_name": "Android Developer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Hive",
          "Java"
        ],
        "role_id": 4,
        "score": 0.0833,
        "slug": "android-engineer",
        "total_count": 24
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "CI/CD",
          "Jenkins"
        ],
        "role_id": 10,
        "score": 0.0833,
        "slug": "devops-engineer",
        "total_count": 24
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 0.98,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.98,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Large-scale distributed data processing",
      "Big data platform engineering",
      "Batch processing and job scheduling",
      "Performance troubleshooting and cluster operations",
      "Hadoop ecosystem engineering",
      "Data security, management, and governance"
    ],
    "matched_kras": [
      "Building large scale distributed data processing systems/application",
      "Develop Big Data solution at enterprise scale",
      "End to end implementation",
      "Work with Cloudera on open issues",
      "Implement cluster configuration changes as needed",
      "Performance analysis, troubleshooting and resolution",
      "Knowledge of Hadoop Security, Data Management and Governance"
    ],
    "matched_skills": [
      "Java",
      "J2EE",
      "Scala",
      "Spark",
      "Hadoop",
      "HDFS",
      "YARN",
      "Confluent Kafka",
      "Hbase",
      "Hive",
      "Impala",
      "NoSQL",
      "AutoSys",
      "Cloudera",
      "Elasticsearch",
      "Sqoop",
      "Oracle",
      "Linux",
      "Git",
      "Jenkins CI/CD"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches Data Engineer.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 160,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 8461,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "HDFS",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8462,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "YARN",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8463,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Confluent Kafka",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8464,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Impala",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8465,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Batch Processing",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8466,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "AutoSys",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8467,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Cloudera",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8468,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8469,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Sqoop",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8470,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Oracle",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 8471,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Linux",
        "status": "pending"
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    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
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}
API 2 — extract-details
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      "alias_persisted": false,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2511,
      "existing_alias_text": "J2EE",
      "input_term": "J2EE",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "sub_category_id": 1172,
        "typical_lifespan": "SHORT_LIVED",
        "volatility": "DEPRECATED"
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      "matched_via": "alias"
    },
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 272,
      "existing_alias_text": "Scala",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
      "input_term": "Spark",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2010,
      "existing_alias_text": "Hadoop",
      "input_term": "Hadoop",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2011,
      "existing_alias_text": "HBase",
      "input_term": "HBase",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "sub_category_id": 31,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 4198,
      "existing_alias_text": "Hive",
      "input_term": "Hive",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1989,
      "existing_alias_text": "NoSQL",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 271,
      "existing_alias_text": "SQL",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
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        "sub_category_id": 97,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 4648,
      "existing_alias_text": "Elasticsearch",
      "input_term": "Elasticsearch",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1613,
      "existing_alias_text": "Git",
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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      "alias_persisted": false,
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      "existing_alias_text": "Jenkins",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "volatility": "STABLE"
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      "matched_via": "alias"
    },
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1826,
      "existing_alias_text": "CI/CD",
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        "is_also_category": false,
        "is_extractable": true,
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        "volatility": "STABLE"
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      "matched_via": "alias"
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      "role_archetype": "Engineering",
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      "slug": "android-engineer",
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      "slug": "pega-developer",
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        "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"
      },
      "input_skill": "Scala",
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      "roles_from_db": [
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          "display_name": "ML Engineer",
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          "rationale": null,
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          "slug": "ml-engineer",
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        },
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          "display_name": "MLOps Engineer",
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          "slug": "ml-ops-engineer",
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      ]
    },
    {
      "dimension": {
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        "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.",
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        "source": "db"
      },
      "input_skill": "Spark",
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      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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          "rationale": null,
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        }
      ]
    },
    {
      "dimension": {
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        "display_name": "ETL and ELT Tooling",
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        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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        "source": "db"
      },
      "input_skill": "Hadoop",
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      "roles_from_db": [
        {
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        }
      ]
    },
    {
      "dimension": {
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        "display_name": "Cloud Storage and Data Services",
        "id": 144,
        "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
        "slug": "cloud-storage-and-data-services",
        "source": "db"
      },
      "input_skill": "HBase",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "Cloud Architect",
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          "rationale": null,
          "role_archetype": null,
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      ]
    },
    {
      "dimension": {
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        "display_name": "Local Persistence and Offline Behavior",
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        "rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
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        "source": "db"
      },
      "input_skill": "Hive",
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      "roles_from_db": [
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          "rationale": null,
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          "slug": "android-engineer",
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        },
        {
          "display_name": "Flutter Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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        {
          "display_name": "Hybrid Mobile Developer",
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          "rationale": null,
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          "slug": "hybrid-mobile-developer",
          "source": "db"
        },
        {
          "display_name": "Native Mobile Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
          "display_name": "React Native Developer",
          "id": 73,
          "rationale": null,
          "role_archetype": "Engineering",
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        {
          "display_name": "iOS Developer",
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          "rationale": null,
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      ]
    },
    {
      "dimension": {
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        "rationale": "Models and manages data using non-relational database systems.",
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      "input_skill": "NoSQL",
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      "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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    },
    {
      "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.",
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        "source": "db"
      },
      "input_skill": "SQL",
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      "roles_from_db": [
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          "display_name": "Pega Developer",
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          "role_archetype": null,
          "slug": "pega-developer",
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      ]
    },
    {
      "dimension": {
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        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "SQL",
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    },
    {
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        "display_name": "Magento Search and Indexing",
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        "rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
        "slug": "magento-search-and-indexing",
        "source": "db"
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      "input_skill": "Elasticsearch",
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      "roles_from_db": [
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          "display_name": "Magento Dev",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
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      ]
    },
    {
      "dimension": {
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        "display_name": "Search and Content Discovery",
        "id": 356,
        "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
        "slug": "search-and-content-discovery",
        "source": "db"
      },
      "input_skill": "Elasticsearch",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Drupal Dev",
          "id": 228,
          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        },
        {
          "display_name": "Sitecore Dev",
          "id": 233,
          "rationale": null,
          "role_archetype": "Engineering",
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      ]
    },
    {
      "dimension": {
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        "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"
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      "input_skill": "Git",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "CI/CD Pipeline Platforms",
        "id": 150,
        "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
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        "source": "db"
      },
      "input_skill": "Jenkins",
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      ]
    },
    {
      "dimension": {
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        "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
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          "alias_text": "JDK 19",
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          "alias_text": "Java 1.4",
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          "alias_text": "Java 1.6",
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          "alias_text": "Java 11",
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          "alias_text": "Java 12",
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          "alias_text": "Java 15",
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          "alias_text": "Java 16",
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          "alias_text": "Java 6",
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          "id": 2977,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Java21",
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          "id": 2978,
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          "match_strategy": "CASE_INSENSITIVE"
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        {
          "alias_text": "Java8",
          "alias_type": "VERSION",
          "id": 2971,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 11",
          "alias_type": "VERSION",
          "id": 21,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 17",
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          "id": 22,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "OpenJDK 21",
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          "id": 23,
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          "match_strategy": "CASE_INSENSITIVE"
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        {
          "alias_text": "OpenJDK 8",
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          "id": 20,
          "is_primary": false,
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        },
        {
          "alias_text": "java 11",
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        {
          "alias_text": "java 17",
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          "id": 1513,
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          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "java 21",
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          "roles_from_db": []
        }
      ],
      "input_skill": "Git",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Jenkins",
          "alias_type": "CANONICAL",
          "id": 544,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Jenkins",
        "id": 283,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "jenkins",
        "sub_category_id": 184,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "Jenkins",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "Jenkins",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Jenkins",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "CI/CD",
          "alias_type": "CANONICAL",
          "id": 1826,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "CI/CD",
        "id": 1190,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "ci-cd",
        "sub_category_id": 900,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD Pipeline Platforms",
            "id": 150,
            "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
            "slug": "ci-cd-pipeline-platforms",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "CI/CD for Machine Learning",
            "id": 56,
            "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
            "slug": "ci-cd-for-machine-learning",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "CI/CD",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "HDFS",
    "YARN",
    "Confluent Kafka",
    "Impala",
    "Batch Processing",
    "AutoSys",
    "Cloudera",
    "ETL",
    "Sqoop",
    "Oracle",
    "Linux"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on large-scale distributed data processing, big data platforms, and ETL-style engineering with Spark/Hadoop/Kafka, which best matches Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "J2EE",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "HDFS",
      "tag": "new"
    },
    {
      "skill": "YARN",
      "tag": "new"
    },
    {
      "skill": "Confluent Kafka",
      "tag": "new"
    },
    {
      "skill": "HBase",
      "tag": "in_db"
    },
    {
      "skill": "Hive",
      "tag": "in_db"
    },
    {
      "skill": "Impala",
      "tag": "new"
    },
    {
      "skill": "NoSQL",
      "tag": "in_db"
    },
    {
      "skill": "Batch Processing",
      "tag": "new"
    },
    {
      "skill": "AutoSys",
      "tag": "new"
    },
    {
      "skill": "Cloudera",
      "tag": "new"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Elasticsearch",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Sqoop",
      "tag": "new"
    },
    {
      "skill": "Oracle",
      "tag": "new"
    },
    {
      "skill": "Linux",
      "tag": "new"
    },
    {
      "skill": "Git",
      "tag": "in_db"
    },
    {
      "skill": "Jenkins",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Java Language and JVM",
          "id": 279,
          "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
          "slug": "java-language-and-jvm",
          "source": "db"
        },
        "dimension_id": 279,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Kotlin and Java",
          "id": 161,
          "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
          "slug": "kotlin-and-java",
          "source": "db"
        },
        "dimension_id": 161,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Android Developer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "android-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Native Mobile Languages",
          "id": 274,
          "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
          "slug": "native-mobile-languages",
          "source": "db"
        },
        "dimension_id": 274,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Application Frameworks",
          "id": 2,
          "rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
          "slug": "web-application-frameworks",
          "source": "db"
        },
        "dimension_id": 2,
        "input_skill": "J2EE",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1565,
        "skill_tag": "in_db",
        "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": "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": "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": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "HBase",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1352,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Local Persistence and Offline Behavior",
          "id": 85,
          "rationale": "On-device storage used for caching, offline support, and durable client state. This cluster is coherent because iOS apps often need to preserve user progress and data when connectivity is limited.",
          "slug": "local-persistence-and-offline-behavior",
          "source": "db"
        },
        "dimension_id": 85,
        "input_skill": "Hive",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Android Developer",
            "id": 4,
            "rationale": null,
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            "source": "db"
          },
          {
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            "rationale": null,
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            "source": "db"
          },
          {
            "display_name": "Hybrid Mobile Developer",
            "id": 11,
            "rationale": null,
            "role_archetype": null,
            "slug": "hybrid-mobile-developer",
            "source": "db"
          },
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          },
          {
            "display_name": "React Native Developer",
            "id": 73,
            "rationale": null,
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            "slug": "react-native-developer",
            "source": "db"
          },
          {
            "display_name": "iOS Developer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "ios-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2754,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
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          "source": "db"
        },
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
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            "rationale": null,
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        ],
        "skill_dimension_saved": true,
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        "dimension_id": 267,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
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          "source": "db"
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        "dimension_id": 21,
        "input_skill": "SQL",
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        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
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        ],
        "skill_dimension_saved": true,
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        "skipped_reason": null
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          "rationale": "Index-driven storefront data freshness and search behavior. This cluster is coherent because Magento commerce sites depend on indexing to keep catalog, price, and search results aligned with changing data.",
          "slug": "magento-search-and-indexing",
          "source": "db"
        },
        "dimension_id": 403,
        "input_skill": "Elasticsearch",
        "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": "Magento Dev",
            "id": 231,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "magento-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 3171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Search and Content Discovery",
          "id": 356,
          "rationale": "Implementing site search, indexing, and content discovery features in Drupal. This cluster is coherent because many Drupal sites need structured search experiences beyond basic navigation.",
          "slug": "search-and-content-discovery",
          "source": "db"
        },
        "dimension_id": 356,
        "input_skill": "Elasticsearch",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Drupal Dev",
            "id": 228,
            "rationale": null,
            "role_archetype": "Engineering",
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          },
          {
            "display_name": "Sitecore Dev",
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            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
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          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Git",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1002,
        "skill_tag": "in_db",
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        "dimension_id": 150,
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
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            "role_archetype": null,
            "slug": "devops-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 283,
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          }
        ],
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      {
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
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            "slug": "devops-engineer",
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        ],
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          "slug": "ci-cd-for-machine-learning",
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        "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",
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            "slug": "ml-engineer",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
        "skill_tag": "in_db",
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      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "e13504ac-8d10-421c-aa7d-e7b997c4d80d"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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