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Pipeline run

0b63dd3e-d50f-42e4-adb7-111d74408b97

Pipeline LLM cost (USD)
API 1: $0.0086 API 2: $0.0003 API 3: $0.0000 Total: $0.0089

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Build Scala/Spark jobs and pipelines to transform and aggregate data, write tests and Scaladoc, and tune Spark/SQL performance across warehouse/storage integrations.
"Create Scala/Spark jobs for data transformation and aggregation"
Tech stack maturity
Mainstream Modern
The skill set centers on Apache Spark, Scala, SQL, and testing tools like ScalaTest/Mockito, which are common in modern data engineering but not specifically cloud-native or bleeding-edge.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (21)
Scala Apache Spark Scalatest JUnit Mockito SQL Embedded Cassandra Spark RDD API Spark SQL DataFrame API Spark MLlib Spark GraphX Spark Streaming Microsoft SQL Server Oracle Database PostgreSQL MySQL HDFS Amazon S3 Cassandra Amazon DynamoDB Distributed Systems
Skill cluster (9 dimension groups, role-scoped)
Programming Languages for Data Work
Scala SQL
Relational Database Usage
PostgreSQL MySQL
Cloud Platforms
Distributed Systems
Cloud Storage and File Formats
Amazon S3
ETL and ELT Tooling
Apache Spark
Relational Database Design
Oracle Database
Stream Processing Systems
Spark Streaming
Testing and Verification
Mockito
Cross-cutting / unaligned
Scalatest JUnit Embedded Cassandra Spark RDD API Spark SQL DataFrame API Spark MLlib Spark GraphX Microsoft SQL Server HDFS Cassandra Amazon DynamoDB
Show KRA description ↓
Create Scala/Spark jobs for data transformation and aggregation Produce unit tests for Spark transformations and helper methods Write Scaladoc-style documentation with all code Design data processing pipelines Scala (with a focus on the functional programming paradigm) Scalatest, JUnit, Mockito {{ , Embedded Cassandra }} Apache Spark 2.x {{ Apache Spark RDD API }} {{ Apache Spark SQL DataFrame API }} {{ Apache Spark MLlib API }} {{ Apache Spark GraphX API }} {{ Apache Spark Streaming API }} Spark query tuning and performance optimization Data Modelling, Data Warehousing SQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }} Experience working with {{ HDFS, S3, Cassandra, and/or DynamoDB }} Deep understanding of distributed systems (e.g. CAP theorem, partitioning, replication, consistency, and consensus)

Signals

Skill data-engineer
0.50
Alias data-engineer
1.00
KRA data-engineer
0.55

Post-classification

Centroidupdated · n=193
Alias collision log
New-role queue
New skills captured8
New KRA captured

Captured for admin review

JUnit primary Data Engineer pending
Embedded Cassandra Data Engineer pending
Spark RDD API Data Engineer pending
Spark SQL DataFrame API Data Engineer pending
Spark MLlib Data Engineer pending
Spark GraphX Data Engineer pending
Microsoft SQL Server Data Engineer pending
HDFS Data Engineer pending
Status: completed Created: 2026-05-27T14:36:54.928017Z Updated: 2026-06-12T17:31:21.306181Z API 3 duration: 43578 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

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

Domain=Data Engineering & Analytics; The JD is centered on building Spark/Scala data transformation jobs, tuning performance, designing data processing pipelines, and integrating with storage/databases, which best matches Data Engineer.

Matched skills

ScalaSparkScalatestJUnitMockitoEmbedded CassandraApache Spark 2.xApache Spark RDD APIApache Spark SQL DataFrame APIApache Spark MLlib APIApache Spark GraphX APIApache Spark Streaming APISpark query tuningSQLHDFSS3CassandraDynamoDB

Matched dimensions

Spark-based data transformation and aggregationData pipeline designUnit testing and code qualityDocumentation and developer supportPerformance optimizationData modeling and data warehousingDistributed systems engineeringSQL/database and storage integration

Matched KRAs

Create Scala/Spark jobs for data transformation and aggregationProduce unit tests for Spark transformations and helper methodsWrite Scaladoc-style documentation with all codeDesign data processing pipelinesSpark query tuning and performance optimizationData Modelling, Data WarehousingSQL database integrationExperience working with HDFS, S3, Cassandra, and/or DynamoDB

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

Spark & Scala Developer
Exp- 5-8 yrs
Shift- Malaysian
Remote - One Position
Onsite (Malaysia) - One Position


Full time Contract Opportunity
Fully Remote


please share resume at Khalil@weradioactive.com


Responsibilities-
Create Scala/Spark jobs for data transformation and aggregation
Produce unit tests for Spark transformations and helper methods
Write Scaladoc-style documentation with all code
Design data processing pipelines


Skills-
Scala (with a focus on the functional programming paradigm)
Scalatest, JUnit, Mockito {{ , Embedded Cassandra }}
Apache Spark 2.x
{{ Apache Spark RDD API }}
{{ Apache Spark SQL DataFrame API }}
{{ Apache Spark MLlib API }}
{{ Apache Spark GraphX API }}
{{ Apache Spark Streaming API }}
Spark query tuning and performance optimization
Data Modelling, Data Warehousing
SQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }}
Experience working with {{ HDFS, S3, Cassandra, and/or DynamoDB }}
Deep understanding of distributed systems (e.g. CAP theorem, partitioning, replication, consistency, and consensus)

Skills from this JD

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

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)
Apache 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
Scalatest Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: ScalaTest id=4492 · scalatest

Aliases — catalog

  • ScalaTest (CANONICAL) primary

Context tags (catalog)

JUnit ScalaCheck assertions behavior-driven development code coverage functional testing integration testing matchers mocking sbt specs2 test cases test suites test-driven development unit testing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Testing Framework
Vendor
ScalaTest
License
apache_2
Year introduced
2009
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: ScalaTest appears in Scala-focused job ads and JVM testing stacks, but JD volume is far below JUnit/TestNG and it’s largely confined to Scala projects rather than broad hiring pipelines.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
770
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Testing and Quality Assurance Catalog dimension db id 12

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Testing and Quality Assurance
testing-and-quality-assurance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JUnit 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
Testing Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Mockito Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Mockito id=2271 · mockito

Aliases — catalog

  • Mockito (CANONICAL) primary

Context tags (catalog)

BDD JUnit Java Mockito annotations Spring assertions behavior verification dependency injection integration testing mock mock creation mocking spy stubbing test doubles test-driven development testing unit testing verification

Stored enrichment (catalog DB)

Category
Library
Sub-category
Mocking Library
Vendor
Mockito
License
apache_2
Year introduced
2007
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Mockito is a standard Java testing library, commonly listed in Java/Spring job descriptions and widely used in enterprise test suites; no vendor sunset or replacement trend is evident.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
2252
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Flutter Testing Frameworks Catalog dimension db id 324

    Library dimension (catalog)

    Roles linked in library: Flutter Developer

  • Testing and Verification Catalog dimension db id 284

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, Ruby Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Flutter Testing Frameworks
flutter-testing-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Testing and Verification
testing-and-verification
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embedded Cassandra Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Cassandra id=92 · apache-cassandra

Aliases — catalog

  • Apache Cassandra (CANONICAL) primary

Context tags (catalog)

CQL NoSQL cluster compaction consistency level data modeling partition key replication factor ring topology secondary index snitch token range tombstones tunable consistency wide-column

Stored enrichment (catalog DB)

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

Maturity reasoning: Apache Cassandra still appears in many production JDs for high-scale distributed storage, and vendors like DataStax continue active support; it’s a common hiring keyword rather than a sunset technology.

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)

  • 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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Spark RDD API Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark GraphX Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark Streaming Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Spark Streaming id=121 · spark-streaming

Aliases — catalog

  • DStreams (VERSION)
  • Spark 2.x (VERSION)
  • Spark 3.x (VERSION)
  • Spark Streaming (VERSION)
  • Spark Structured Streaming (VERSION)
  • Structured Streaming (VERSION)

Context tags (catalog)

DStreams Kafka Kinesis Structured Streaming backpressure checkpointing event time exactly-once micro-batch stateful processing streaming ETL trigger intervals watermarking window functions windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2013
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
Structured Streaming (Spark 2.0+)

Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Stream Processing Systems Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
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
Microsoft SQL Server Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL Server id=18 · sql-server

Aliases — catalog

  • SQL Server (CANONICAL) primary
  • SQL Server 2000 (VERSION)
  • SQL Server 2005 (VERSION)
  • SQL Server 2008 (VERSION)
  • SQL Server 2012 (VERSION)
  • SQL Server 2014 (VERSION)
  • SQL Server 2016 (VERSION)
  • SQL Server 2017 (VERSION)
  • SQL Server 2019 (VERSION)
  • SQL Server 2022 (VERSION)
  • SQL Server 6.5 (VERSION)
  • SQL Server 7.0 (VERSION)

Context tags (catalog)

Always On CLR Integration Clustered Index ETL Execution Plan Linked Servers Query Store Replication SQL Agent SQL Server Agent SQL Server Integration Services SQL Server Management Studio SQL Server Reporting Services SSIS SSMS SSRS Stored Procedures T-SQL TempDB backup and recovery backup and restore clustering data migration data warehousing database design database normalization indexing performance tuning query optimization replication stored procedures transaction log transaction logs

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Microsoft
License
proprietary
Year introduced
1989
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL Server appears in many enterprise job descriptions and remains a major Microsoft-supported RDBMS with active Azure SQL/SQL Server demand; it is a common hiring-pipeline staple, not a sunset technology.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Oracle Database Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Oracle Database id=1566 · oracle-database

Aliases — catalog

  • Oracle Database (CANONICAL) primary

Context tags (catalog)

Backup and Recovery Data Guard Data Warehousing Indexing Oracle APEX Oracle Cloud Oracle Enterprise Manager Oracle Exadata Oracle Forms Oracle Fusion Middleware Oracle RAC Oracle SQL Oracle SQL*Plus PL/SQL Partitioning Performance Tuning RMAN SQL Developer

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
Oracle Corporation
License
proprietary
Year introduced
1979
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Oracle Database appears in many enterprise job postings and remains a standard RDBMS in large regulated environments; Oracle continues active vendor support and releases, indicating broad market demand.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

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)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MySQL id=17 · mysql

Aliases — catalog

  • MySQL (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

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

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HDFS Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Amazon S3 Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon S3 id=170 · amazon-s3

Aliases — catalog

  • Amazon S3 (CANONICAL) primary

Context tags (catalog)

ACL Cross-Region Replication Glacier SSE-KMS SSE-S3 access control bucket bucket policy cross-region replication event notifications lifecycle policy multipart upload object storage pre-signed URL replication static website hosting storage class versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2006
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
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

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

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)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Cassandra Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cassandra id=1354 · cassandra

Aliases — catalog

  • Cassandra (CANONICAL)

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon DynamoDB Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon DynamoDB id=93 · amazon-dynamodb

Aliases — catalog

  • Amazon DynamoDB (CANONICAL) primary

Context tags (catalog)

AWS SDK DAX GSI LSI NoSQL Streams TTL boto3 conditional writes on-demand capacity partition key provisioned throughput secondary index sort key transactions

Stored enrichment (catalog DB)

Category
Service
Sub-category
Managed Nosql Database Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in cloud/backend job descriptions and widely used on AWS; strong vendor adoption and active ecosystem signal broad market demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
55
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)
Distributed Systems Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Distributed Systems id=1369 · distributed-systems

Aliases — catalog

  • Distributed Systems (CANONICAL)

Context tags (catalog)

CAP theorem Docker Swarm Kafka MapReduce Zookeeper consensus algorithms distributed databases eventual consistency fault tolerance gRPC load balancing message queues microservices replication sharding

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Distributed Systems
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1035
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Performance and Scalability Tuning Catalog dimension db id 11

    Library dimension (catalog)

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

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Performance and Scalability Tuning
performance-and-scalability-tuning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
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
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)
Apache Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Scalatest in_db
Testing and Quality Assurance
testing-and-quality-assurance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Mockito in_db
Flutter Testing Frameworks
flutter-testing-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Mockito in_db
Testing and Verification
testing-and-verification
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embedded Cassandra new
NoSQL Databases
nosql-databases
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Spark Streaming in_db
Stream Processing Systems
stream-processing-systems
Existing dimension (library) · Role↔dimension saved
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
Microsoft SQL Server new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Oracle Database in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Oracle Database in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MySQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon S3 in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
Cassandra in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Amazon DynamoDB in_db
NoSQL Databases
nosql-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Systems in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Distributed Systems in_db
Performance and Scalability Tuning
performance-and-scalability-tuning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Distributed Systems in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed JUnit | type=Testing Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Spark RDD API | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Spark SQL DataFrame API | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Spark MLlib | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Spark GraphX | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed HDFS | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed Embedded Cassandra ↔ NoSQL Databases
dimension_skill_link_proposed Microsoft SQL Server ↔ Relational Database Design
nano JD Parser — gpt-4.1-nano click to toggle
RoleSpark & Scala Developer
Experience5-8 yrs
DomainOther
Location Malaysia (remote)
JD type pass
Show raw JSON
{
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  "about_company": null,
  "certifications": [],
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  "ctc": null,
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    },
    "secondary": null
  },
  "education": [],
  "experience": {
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    "min": 5,
    "raw": "5-8 yrs"
  },
  "job_locations": [
    {
      "aliases": [],
      "city": null,
      "country": "Malaysia",
      "state": null,
      "work_mode": "remote"
    },
    {
      "aliases": [],
      "city": null,
      "country": "Malaysia",
      "state": null,
      "work_mode": "onsite"
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  ],
  "role": "Spark \u0026 Scala Developer",
  "role_aliases": [
    "Scala Developer",
    "Spark Developer",
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  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 4,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Create Scala/Spark jobs for data",
        "last_5_words": "data processing pipelines"
      },
      "text": "Create Scala/Spark jobs for data transformation and aggregation\nProduce unit tests for Spark transformations and helper methods\nWrite Scaladoc-style documentation with all code\nDesign data processing pipelines",
      "word_count": 40
    },
    {
      "bullet_count": 12,
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      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Scala (with a focus on",
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      },
      "text": "Scala (with a focus on the functional programming paradigm)\nScalatest, JUnit, Mockito {{ , Embedded Cassandra }}\nApache Spark 2.x\n{{ Apache Spark RDD API }}\n{{ Apache Spark SQL DataFrame API }}\n{{ Apache Spark MLlib API }}\n{{ Apache Spark GraphX API }}\n{{ Apache Spark Streaming API }}\nSpark query tuning and performance optimization\nData Modelling, Data Warehousing\nSQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }}\nExperience working with {{ HDFS, S3, Cassandra, and/or DynamoDB }}\nDeep understanding of distributed systems (e.g. CAP theorem, partitioning, replication, consistency, and consensus)",
      "word_count": 118
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Spark"
    },
    {
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    },
    {
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    },
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    {
      "is_primary": false,
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    },
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    },
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      "is_primary": false,
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    },
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    },
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    },
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      "is_primary": false,
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    },
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    },
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      },
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        },
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        "word_count": 118
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "0b63dd3e-d50f-42e4-adb7-111d74408b97",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Scala Backend Developer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 87,
        "score": 1.0,
        "slug": "scala-backend-developer",
        "total_count": null
      }
    ],
    "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.",
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            "similarity": 0.6049
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          },
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            "similarity": 0.5204
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.5545,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Svelte Frontend Developer",
        "kra_matches": [
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            "kra_text": "performance tuning",
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            "similarity": 0.5876
          },
          {
            "kra_text": "backend data integration",
            "sentence": "SQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }}",
            "similarity": 0.5394
          },
          {
            "kra_text": "backend data integration",
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            "similarity": 0.412
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 92,
        "score": 0.513,
        "slug": "svelte-frontend-developer",
        "total_count": null
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": [
          {
            "kra_text": "Writes database access logic including SQL queries, ORM mappings, stored procedures, and migration scripts for relational databases like PostgreSQL and MySQL.",
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            "similarity": 0.5614
          },
          {
            "kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
            "sentence": "Spark query tuning and performance optimization",
            "similarity": 0.5023
          },
          {
            "kra_text": "Identifies and resolves backend performance bottlenecks through query optimization, indexing strategies, connection pooling, and distributed caching with Redis.",
            "sentence": "Scalatest, JUnit, Mockito {{ , Embedded Cassandra }}",
            "similarity": 0.3928
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 0.4855,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "backend performance tuning",
            "sentence": "Spark query tuning and performance optimization",
            "similarity": 0.6043
          },
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            "kra_text": "asynchronous job processing",
            "sentence": "Create Scala/Spark jobs for data transformation and aggregation",
            "similarity": 0.3994
          },
          {
            "kra_text": "external system integration",
            "sentence": "SQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }}",
            "similarity": 0.3819
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.4619,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Optimizes application performance from database query efficiency through API response latency to frontend rendering speed and bundle size.",
            "sentence": "Spark query tuning and performance optimization",
            "similarity": 0.4865
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "SQL database integration {{ Microsoft, Oracle, Postgres, and/or MySQL }}",
            "similarity": 0.4763
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Experience working with {{ HDFS, S3, Cassandra, and/or DynamoDB }}",
            "similarity": 0.4206
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.4611,
        "slug": "full-stack-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Apache Spark",
          "SQL",
          "Scala"
        ],
        "role_id": 2,
        "score": 0.5,
        "slug": "data-engineer",
        "total_count": 6
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Scala"
        ],
        "role_id": 3,
        "score": 0.1667,
        "slug": "ml-engineer",
        "total_count": 6
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Scala"
        ],
        "role_id": 16,
        "score": 0.1667,
        "slug": "ml-ops-engineer",
        "total_count": 6
      },
      {
        "display_name": "Pega Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "SQL"
        ],
        "role_id": 24,
        "score": 0.1667,
        "slug": "pega-developer",
        "total_count": 6
      },
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "ScalaTest"
        ],
        "role_id": 1,
        "score": 0.1667,
        "slug": "backend-engineer",
        "total_count": 6
      }
    ]
  },
  "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": [
      "Spark-based data transformation and aggregation",
      "Data pipeline design",
      "Unit testing and code quality",
      "Documentation and developer support",
      "Performance optimization",
      "Data modeling and data warehousing",
      "Distributed systems engineering",
      "SQL/database and storage integration"
    ],
    "matched_kras": [
      "Create Scala/Spark jobs for data transformation and aggregation",
      "Produce unit tests for Spark transformations and helper methods",
      "Write Scaladoc-style documentation with all code",
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      "Spark query tuning and performance optimization",
      "Data Modelling, Data Warehousing",
      "SQL database integration",
      "Experience working with HDFS, S3, Cassandra, and/or DynamoDB"
    ],
    "matched_skills": [
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      "Scalatest",
      "JUnit",
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      "Embedded Cassandra",
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      "Apache Spark MLlib API",
      "Apache Spark GraphX API",
      "Apache Spark Streaming API",
      "Spark query tuning",
      "SQL",
      "HDFS",
      "S3",
      "Cassandra",
      "DynamoDB"
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    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building Spark/Scala data transformation jobs, tuning performance, designing data processing pipelines, and integrating with storage/databases, which best matches Data Engineer.",
    "sub_role": null
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  "stage5_updates": {
    "centroid_n_after": 193,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
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        "is_primary": true,
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        "role_slug": "data-engineer",
        "skill_name": "JUnit",
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        "is_primary": false,
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        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Embedded Cassandra",
        "status": "pending"
      },
      {
        "is_primary": false,
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        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark RDD API",
        "status": "pending"
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      {
        "is_primary": false,
        "queue_id": 10005,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark SQL DataFrame API",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 10006,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark MLlib",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 10007,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark GraphX",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 10008,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Microsoft SQL Server",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 10009,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "HDFS",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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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": 2004,
      "existing_alias_text": "Apache Spark",
      "input_term": "Apache Spark",
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        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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      "matched_via": "alias"
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      "alias_persisted": false,
      "existing_alias_id": 6334,
      "existing_alias_text": "ScalaTest",
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      "matched_via": "alias"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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      "matched_via": "embedding_alias"
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      "alias_persisted": false,
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      "existing_alias_text": "Spark Streaming",
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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,
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      "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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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2512,
      "existing_alias_text": "Oracle Database",
      "input_term": "Oracle Database",
      "matched_canonical": {
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        "is_also_category": false,
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        "skill_nature": "TOOL",
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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": 121,
      "existing_alias_text": "PostgreSQL",
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        "is_also_category": false,
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        "skill_nature": "TOOL",
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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,
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      "existing_alias_text": "MySQL",
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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": 379,
      "existing_alias_text": "Amazon S3",
      "input_term": "Amazon S3",
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2013,
      "existing_alias_text": "Cassandra",
      "input_term": "Cassandra",
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        "category_id": 3,
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      "matched_via": "alias"
    },
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      "alias_persisted": false,
      "existing_alias_id": 254,
      "existing_alias_text": "Amazon DynamoDB",
      "input_term": "Amazon DynamoDB",
      "matched_canonical": {
        "category_id": 11,
        "display_name": "Amazon DynamoDB",
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        "skill_nature": "CLOUD_SERVICE",
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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": 2028,
      "existing_alias_text": "Distributed Systems",
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      "matched_canonical": {
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      "matched_via": "alias"
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      "slug": "ml-engineer",
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      "slug": "ml-ops-engineer",
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        },
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        },
        {
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          "rationale": null,
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        {
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          "rationale": null,
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      ]
    },
    {
      "dimension": {
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            "display_name": "Relational Data Modeling",
            "id": 216,
            "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
            "slug": "relational-data-modeling",
            "source": "db"
          },
          "input_skill": "MySQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "MySQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Usage",
            "id": 371,
            "rationale": "Working effectively with operational relational databases from Go backend services. This includes schema-aware querying, indexing awareness, transactions, and understanding how service code interacts with PostgreSQL or similar systems.",
            "slug": "relational-database-usage",
            "source": "db"
          },
          "input_skill": "MySQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "MySQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "HDFS",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "hdfs",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Amazon S3",
          "alias_type": "CANONICAL",
          "id": 379,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "Amazon S3",
        "id": 170,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-s3",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Amazon S3",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Storage and File Formats",
            "id": 35,
            "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
            "slug": "cloud-storage-and-file-formats",
            "source": "db"
          },
          "input_skill": "Amazon S3",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Amazon S3",
      "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": "Cassandra",
          "alias_type": "CANONICAL",
          "id": 2013,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 3,
        "display_name": "Cassandra",
        "id": 1354,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "cassandra",
        "sub_category_id": 31,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Cassandra",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Cassandra",
      "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": "Amazon DynamoDB",
          "alias_type": "CANONICAL",
          "id": 254,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "Amazon DynamoDB",
        "id": 93,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "amazon-dynamodb",
        "sub_category_id": 55,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "NoSQL Databases",
            "id": 19,
            "rationale": "Models and manages data using non-relational database systems.",
            "slug": "nosql-databases",
            "source": "db"
          },
          "input_skill": "Amazon DynamoDB",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Amazon DynamoDB",
      "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": "Distributed Systems",
          "alias_type": "CANONICAL",
          "id": 2028,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Distributed Systems",
        "id": 1369,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "distributed-systems",
        "sub_category_id": 1035,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms",
            "id": 20,
            "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
            "slug": "cloud-platforms",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Performance and Scalability Tuning",
            "id": 11,
            "rationale": "Techniques for improving throughput, latency, and resource efficiency in PHP backend services. This includes profiling, query optimization, concurrency limits, memory use, and bottleneck analysis.",
            "slug": "performance-and-scalability-tuning",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Distributed Systems",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Distributed Systems",
      "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": [
    "JUnit",
    "Spark RDD API",
    "Spark SQL DataFrame API",
    "Spark MLlib",
    "Spark GraphX",
    "HDFS"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on building Spark/Scala data transformation jobs, tuning performance, designing data processing pipelines, and integrating with storage/databases, which best matches Data Engineer.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "Apache Spark",
      "tag": "in_db"
    },
    {
      "skill": "Scalatest",
      "tag": "in_db"
    },
    {
      "skill": "JUnit",
      "tag": "new"
    },
    {
      "skill": "Mockito",
      "tag": "in_db"
    },
    {
      "skill": "Embedded Cassandra",
      "tag": "in_db"
    },
    {
      "skill": "Spark RDD API",
      "tag": "new"
    },
    {
      "skill": "Spark SQL DataFrame API",
      "tag": "new"
    },
    {
      "skill": "Spark MLlib",
      "tag": "new"
    },
    {
      "skill": "Spark GraphX",
      "tag": "new"
    },
    {
      "skill": "Spark Streaming",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft SQL Server",
      "tag": "in_db"
    },
    {
      "skill": "Oracle Database",
      "tag": "in_db"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "MySQL",
      "tag": "in_db"
    },
    {
      "skill": "HDFS",
      "tag": "new"
    },
    {
      "skill": "Amazon S3",
      "tag": "in_db"
    },
    {
      "skill": "Cassandra",
      "tag": "in_db"
    },
    {
      "skill": "Amazon DynamoDB",
      "tag": "in_db"
    },
    {
      "skill": "Distributed Systems",
      "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": "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": "Apache 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,
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          {
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}

LLM Calls

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

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