Pipeline run

569a157f-9ebc-4d47-8389-d2139dbd3178

Pipeline LLM cost (USD)

API 1: $0.0081 API 2: $0.0001 API 3: $0.0000 Total: $0.0082

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

Nature of work · Data Engineering / Streaming Platforms

Build and tune low-latency real-time data pipelines for a cash equities trading platform, using Java/Scala with Kafka, Beam, and Dataflow; also drive end-to-end delivery, performance optimization, and team mentoring.

"Engineer the data transformations and analysis for the Cash Equities Trading platform."

Tech stack maturity

Mainstream Modern

Apache Beam and Apache Kafka in Java/Scala are widely adopted, current streaming technologies used in modern data platforms, but not typically indicative of bleeding-edge AI-native or legacy stacks.

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

0.20 / 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): fine-tuning

Evidence — skills matched in JD (7)

Java Scala Apache Beam Google Dataflow Apache Kafka Apache Flink Apache Spark

Skill cluster (3 dimension groups, role-scoped)

Java Language and JVM

Java

Programming Languages for Data Work

Scala

Cross-cutting / unaligned

Apache Beam Google Dataflow Apache Kafka Apache Flink Apache Spark

Show KRA description ↓

• Engineer the data transformations and analysis for the Cash Equities Trading platform. • Technology SME on the real-time stream processing paradigm. • Bring your experience in Low latency, High through-put, auto scaling platform design and implementation. • Implementing an end-to-end platform service, assessing the operations and non-functional needs clearly. • Mentor and Coach the engineering and SME talent to realize their potential and build a high-performance team. • Manage complex end to end functional transformation module from planning estimations to execution. • Improve the platform standards by bringing in new ideas and solutions on the table. • 12+ years of experience in data engineering technology and tools. • Must have experience with Java / Scala based implementations for enterprise-wide platforms. • Experience with Apache Beam, Google Dataflow, Apache Kafka for real-time steam processing technology stack. • Complex state-full processing of events with partitioning for higher throughputs. • Have dealt with fine-tuning the through-puts and improving the performance aspects on data pipelines. • Experience with analytical data store optimizations, querying and managing them. • Experience with alternate data engineering tools (Apache Flink, Apache Spark etc). • Reason and have an ability to convince the stake holders and wider technology team about your decisions. • Set highest standards of integrity and ethics and lead with examples on technology implementations.

Signals

Skill data-engineer

0.80

Alias —

—

KRA data-engineer

0.53

Post-classification

Centroidupdated · n=9

Alias collision log—

New-role queue—

New skills captured1

New KRA capturedyes

Captured for admin review

Google Dataflow primary ↔ Streaming / Real-Time Data Engineer pending

R&R fragment (sim 0.00) ↔ Streaming / Real-Time Data Engineer pending

Status: completed Created: 2026-05-27T16:26:06.190234Z Updated: 2026-05-27T16:27:13.236974Z API 3 duration: 30030 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

Streaming / Real-Time Data Engineer

domain · Data Engineering & Analytics CASE DOMAIN

slug: streaming-real-time-data-engineer · id: 149 · source: db

Domain=Data Engineering & Analytics; The JD is centered on real-time stream processing, low-latency/high-throughput platform design, Kafka/Beam/Dataflow, and stateful event processing, which best fits a Streaming / Real-Time Data Engineer.

Matched skills

JavaScalaApache BeamGoogle DataflowApache KafkaApache FlinkApache Sparkstream processingreal-time stream processingstate-full processingpartitioninganalytical data store optimizations

Matched dimensions

Real-time streaming platform engineeringLow-latency and high-throughput systems designEnd-to-end data platform implementationPipeline performance tuningAnalytical data store optimizationTechnical leadership and mentoringStakeholder influence

Matched KRAs

Engineer the data transformations and analysisTechnology SME on the real-time stream processing paradigmBring experience in low latency, high through-put platform designImplementing an end-to-end platform serviceManage complex end to end functional transformation moduleImprove the platform standards by bringing in new ideasMentor and Coach the engineering and SME talent

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

New skills

Skill↔dim saved

Role↔dim saved

Skipped

Job description

Job Description

Some careers shine brighter than others.

If you’re looking for a career that will help you stand out, join HSBC and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.

HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of Sr. Associate Director, Data and Analytics

In this role, you will:

• Engineer the data transformations and analysis for the Cash Equities Trading platform.
• Technology SME on the real-time stream processing paradigm.
• Bring your experience in Low latency, High through-put, auto scaling platform design and implementation.
• Implementing an end-to-end platform service, assessing the operations and non-functional needs clearly.
• Mentor and Coach the engineering and SME talent to realize their potential and build a high-performance team.
• Manage complex end to end functional transformation module from planning estimations to execution.
• Improve the platform standards by bringing in new ideas and solutions on the table.


Requirements

To be successful in this role, you should meet the following requirements:

• 12+ years of experience in data engineering technology and tools.
• Must have experience with Java / Scala based implementations for enterprise-wide platforms.
• Experience with Apache Beam, Google Dataflow, Apache Kafka for real-time steam processing technology stack.
• Complex state-full processing of events with partitioning for higher throughputs.
• Have dealt with fine-tuning the through-puts and improving the performance aspects on data pipelines.
• Experience with analytical data store optimizations, querying and managing them.
• Experience with alternate data engineering tools (Apache Flink, Apache Spark etc).
• Reason and have an ability to convince the stake holders and wider technology team about your decisions.
• Set highest standards of integrity and ethics and lead with examples on technology implementations.


You’ll achieve more when you join HSBC.

www.hsbc.com/careers

HSBC is committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and opportunities to grow within an inclusive and diverse environment. Personal data held by the Bank relating to employment applications will be used in accordance with our Privacy Statement, which is available on our website.

Issued by – HSBC Software Development India

Skills from this JD

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

Java Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Java id=1 · java

Aliases — catalog

Java (CANONICAL) primary
JDK (VERSION)
JDK 10 (VERSION)
JDK 11 (VERSION)
JDK 12 (VERSION)
JDK 13 (VERSION)
JDK 14 (VERSION)
JDK 15 (VERSION)
JDK 16 (VERSION)
JDK 17 (VERSION)
JDK 18 (VERSION)
JDK 19 (VERSION)
JDK 20 (VERSION)
JDK 21 (VERSION)
JDK 5 (VERSION)
JDK 6 (VERSION)
JDK 7 (VERSION)
JDK 8 (VERSION)
JDK 9 (VERSION)
Java 1.0 (VERSION)
Java 1.1 (VERSION)
Java 1.2 (VERSION)
Java 1.3 (VERSION)
Java 1.4 (VERSION)
Java 1.5 (VERSION)
Java 1.6 (VERSION)
Java 1.7 (VERSION)
Java 1.8 (VERSION)
Java 10 (VERSION)
Java 11 (VERSION)
Java 12 (VERSION)
Java 13 (VERSION)
Java 14 (VERSION)
Java 15 (VERSION)
Java 16 (VERSION)
Java 17 (VERSION)
Java 18 (VERSION)
Java 19 (VERSION)
Java 20 (VERSION)
Java 21 (VERSION)
Java 5 (VERSION)
Java 6 (VERSION)
Java 7 (VERSION)
Java 8 (VERSION)
Java 9 (VERSION)
Java11 (VERSION)
Java17 (VERSION)
Java21 (VERSION)
Java8 (VERSION)
OpenJDK 11 (VERSION)
OpenJDK 17 (VERSION)
OpenJDK 21 (VERSION)
OpenJDK 8 (VERSION)
java 11 (VERSION)
java 17 (VERSION)
java 21 (VERSION)
java 4 (VERSION)
java 5 (VERSION)
java 6 (VERSION)
java 7 (VERSION)
java 8 (VERSION)
java lts (VERSION)
java-11 (VERSION)
java-17 (VERSION)
java-21 (VERSION)
java-4 (VERSION)
java-5 (VERSION)
java-6 (VERSION)
java-7 (VERSION)
java-8 (VERSION)
java11 (VERSION)
java17 (VERSION)
java21 (VERSION)
java4 (VERSION)
java5 (VERSION)
java6 (VERSION)
java7 (VERSION)
java8 (VERSION)
jdk 11 (VERSION)
jdk 17 (VERSION)
jdk 21 (VERSION)
jdk 4 (VERSION)
jdk 5 (VERSION)
jdk 6 (VERSION)
jdk 7 (VERSION)
jdk 8 (VERSION)
jdk11 (VERSION)
jdk17 (VERSION)
jdk21 (VERSION)
jdk4 (VERSION)
jdk5 (VERSION)
jdk6 (VERSION)
jdk7 (VERSION)
jdk8 (VERSION)
jvm21 (VERSION)

Context tags (catalog)

APIs Apache Tomcat Concurrency Design patterns Garbage collection GraalVM Gradle Hibernate JDBC JDK JPA JUnit JVM Java 8 Java EE JavaFX Kafka Lambda expressions Maven Microservices Mockito Object-oriented REST RESTful SOAP Servlets Spring Spring Boot Tomcat microservices

Stored enrichment (catalog DB)

Category: Language
Sub-category: Programming Language
Vendor: Oracle
License: other_open
Year introduced: 1995
Confidence: 0.99
Version strategy: SEPARATE_ENTITY
Version tag: 21

Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

Java Language and JVM Catalog dimension db id 279

Library dimension (catalog)

Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer
Kotlin and Java Catalog dimension db id 161

Library dimension (catalog)

Roles linked in library: Android Developer
Native Mobile Languages Catalog dimension db id 274

Library dimension (catalog)

Roles linked in library: Native Mobile Developer
Pega Programming Languages & DSLs Catalog dimension db id 267

Library dimension (catalog)

Roles linked in library: Pega Developer
Programming Languages Catalog dimension db id 1

Library dimension (catalog)

Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
Programming Languages & DSLs Catalog dimension db id 475

Library dimension (catalog)

Roles linked in library: Engineering Manager
Programming Languages for Data Work Catalog dimension db id 21

Library dimension (catalog)

Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
Java Language and JVM java-language-and-jvm	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kotlin and Java kotlin-and-java	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Native Mobile Languages native-mobile-languages	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pega Programming Languages & DSLs pega-programming-languages-dsls	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages programming-languages	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs programming-languages-dsls	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work programming-languages-for-data-work	✓	—	Existing dimension (library) · Role↔dimension 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 skipped (dimension not under chosen role)
Programming Languages for ML Systems programming-languages-for-ml-systems	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Apache Beam Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Apache Beam id=124 · apache-beam

Aliases — catalog

Apache Beam (CANONICAL) primary

Context tags (catalog)

Dataflow DoFn ETL Flink Kafka PCollection Pub/Sub Spark batch processing pipeline orchestration runner side inputs streaming pipelines watermarks windowing

Stored enrichment (catalog DB)

Category: Framework
Sub-category: Data Processing Framework
Vendor: Apache Software Foundation
License: apache_2
Year introduced: 2016
Confidence: 0.96
Version strategy: NOT_APPLICABLE

Maturity reasoning: Apache Beam appears in some data-engineering JDs, but far less often than Spark/Flink; its ecosystem is smaller and usage is concentrated in streaming/batch pipeline teams.

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)

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 skipped (dimension not under chosen role)

Google Dataflow 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: PLATFORM
Volatility: MEDIUM
Typical lifespan: MULTI_YEAR
Version strategy: UNVERSIONED

Apache Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Apache Kafka id=145 · apache-kafka

Aliases — catalog

Apache Kafka (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

Messaging and Event Streaming Catalog dimension db id 8

Library dimension (catalog)

Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
Messaging and Event Streaming messaging-and-event-streaming	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Apache Flink Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Apache Flink id=120 · apache-flink

Aliases — catalog

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

Context tags (catalog)

CEP DataStream API Flink SQL Kafka Kinesis SQL Table API checkpointing event time exactly-once state backend stateful processing stream processing watermarks windowing

Stored enrichment (catalog DB)

Category: Framework
Sub-category: Stream Processing Framework
Vendor: Apache Software Foundation
License: apache_2
Year introduced: 2014
Confidence: 0.95
Version strategy: SEPARATE_ENTITY
Version tag: 1.20

Maturity reasoning: Apache Flink appears in streaming/data-platform JDs, but far less often than Spark/Kafka; GitHub and job-market signals show a specialized real-time processing niche rather than broad hiring staple.

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 skipped (dimension not under chosen role)

Apache Spark Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)

Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

ETL and ELT Tooling Catalog dimension db id 24

Library dimension (catalog)

Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension	Skill↔dim	Role↔dim	Outcome
ETL and ELT Tooling etl-and-elt-tooling	✓	—	Existing dimension (library) · Role↔dimension 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
Java	in_db	Java Language and JVM java-language-and-jvm	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Kotlin and Java kotlin-and-java	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Native Mobile Languages native-mobile-languages	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Pega Programming Languages & DSLs pega-programming-languages-dsls	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Programming Languages programming-languages	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Programming Languages & DSLs programming-languages-dsls	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java	in_db	Programming Languages for Data Work programming-languages-for-data-work	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scala	in_db	Programming Languages for Data Work programming-languages-for-data-work	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 Beam	in_db	Stream Processing Systems stream-processing-systems	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Kafka	in_db	Messaging and Event Streaming messaging-and-event-streaming	✓	—	Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Flink	in_db	Stream Processing Systems stream-processing-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 skipped (dimension not under chosen role)

Library artifacts (this run)

Kind	Detail	DB id
canonical_skill_proposed	Google Dataflow \| type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR

nano JD Parser — gpt-4.1-nano click to toggle

RoleSr. Associate Director, Data and Analytics

CompanyHSBC

Experience12+ years of experience in data engineering technology and tools.

DomainBanking

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "HSBC is one of the",
      "last_5_words": "hopes and realise their ambitions."
    },
    "text": "HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.",
    "word_count": 50
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  "certifications": [],
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  "education": [],
  "experience": {
    "max": null,
    "min": 12,
    "raw": "12+ years of experience in data engineering technology and tools."
  },
  "job_locations": [],
  "role": "Sr. Associate Director, Data and Analytics",
  "role_aliases": [
    "Associate Director, Data",
    "Data and Analytics Director",
    "Data Engineering Director"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 7,
      "heading": "In this role, you will:",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "In this role, you will:",
        "last_5_words": "new ideas and solutions on the table."
      },
      "text": "\u2022 Engineer the data transformations and analysis for the Cash Equities Trading platform.\n\u2022 Technology SME on the real-time stream processing paradigm.\n\u2022 Bring your experience in Low latency, High through-put, auto scaling platform design and implementation.\n\u2022 Implementing an end-to-end platform service, assessing the operations and non-functional needs clearly.\n\u2022 Mentor and Coach the engineering and SME talent to realize their potential and build a high-performance team.\n\u2022 Manage complex end to end functional transformation module from planning estimations to execution.\n\u2022 Improve the platform standards by bringing in new ideas and solutions on the table.",
      "word_count": 104
    },
    {
      "bullet_count": 9,
      "heading": "Requirements",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Requirements\n\u2022 12+ years of experience",
        "last_5_words": "and lead with examples on technology implementations."
      },
      "text": "\u2022 12+ years of experience in data engineering technology and tools.\n\u2022 Must have experience with Java / Scala based implementations for enterprise-wide platforms.\n\u2022 Experience with Apache Beam, Google Dataflow, Apache Kafka for real-time steam processing technology stack.\n\u2022 Complex state-full processing of events with partitioning for higher throughputs.\n\u2022 Have dealt with fine-tuning the through-puts and improving the performance aspects on data pipelines.\n\u2022 Experience with analytical data store optimizations, querying and managing them.\n\u2022 Experience with alternate data engineering tools (Apache Flink, Apache Spark etc).\n\u2022 Reason and have an ability to convince the stake holders and wider technology team about your decisions.\n\u2022 Set highest standards of integrity and ethics and lead with examples on technology implementations.",
      "word_count": 139
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  ],
  "urls": [
    {
      "type": "careers",
      "url": "www.hsbc.com/careers"
    }
  ]
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Java"
    },
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Beam"
    },
    {
      "is_primary": true,
      "skill_name": "Google Dataflow"
    },
    {
      "is_primary": true,
      "skill_name": "Apache Kafka"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Flink"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Spark"
    }
  ],
  "jd_role": {
    "display_name": "Sr. Associate Director, Data and Analytics",
    "rationale": null,
    "role_aliases": [
      "Associate Director, Data",
      "Data and Analytics Director",
      "Data Engineering Director"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "HSBC is one of the",
        "last_5_words": "hopes and realise their ambitions."
      },
      "text": "HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.",
      "word_count": 50
    },
    "certifications": [],
    "company_name": "HSBC",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "BFSI",
          "Financial Services"
        ],
        "domain": "Banking"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 12,
      "raw": "12+ years of experience in data engineering technology and tools."
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    "new_role_display_name": null,
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}

API 2 — extract-details

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      "matched_via": "alias"
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    {
      "dimension": {
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        "display_name": "Programming Languages for ML Systems",
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        {
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        },
        {
          "alias_text": "jdk21",
          "alias_type": "VERSION",
          "id": 1494,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk4",
          "alias_type": "VERSION",
          "id": 1477,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk5",
          "alias_type": "VERSION",
          "id": 1478,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk6",
          "alias_type": "VERSION",
          "id": 1479,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk7",
          "alias_type": "VERSION",
          "id": 1480,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jdk8",
          "alias_type": "VERSION",
          "id": 1481,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "jvm21",
          "alias_type": "VERSION",
          "id": 1495,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Java",
        "id": 1,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "java",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Java Language and JVM",
            "id": 279,
            "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
            "slug": "java-language-and-jvm",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Java Backend Developer",
              "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"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Android Developer",
              "id": 4,
              "rationale": null,
              "role_archetype": null,
              "slug": "android-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Native Mobile Developer",
              "id": 75,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "native-mobile-developer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Pega Developer",
              "id": 24,
              "rationale": null,
              "role_archetype": null,
              "slug": "pega-developer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "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"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages \u0026 DSLs",
            "id": 475,
            "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
            "slug": "programming-languages-dsls",
            "source": "db"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Java",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Java",
      "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": "Scala",
          "alias_type": "CANONICAL",
          "id": 272,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "Scala",
        "id": 102,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "scala",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Scala",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "Scala",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Scala",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Beam",
          "alias_type": "CANONICAL",
          "id": 328,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Apache Beam",
        "id": 124,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-beam",
        "sub_category_id": 91,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Stream Processing Systems",
            "id": 25,
            "rationale": "Technologies for processing event streams and near-real-time data flows. This includes stream transformations, windowing, stateful processing, and stream-to-warehouse delivery patterns.",
            "slug": "stream-processing-systems",
            "source": "db"
          },
          "input_skill": "Apache Beam",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Beam",
      "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": "Google Dataflow",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PLATFORM",
          "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": "google-dataflow",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Kafka",
          "alias_type": "CANONICAL",
          "id": 349,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Apache Kafka",
        "id": 145,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "apache-kafka",
        "sub_category_id": 128,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Messaging and Event Streaming",
            "id": 8,
            "rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
            "slug": "messaging-and-event-streaming",
            "source": "db"
          },
          "input_skill": "Apache Kafka",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Kafka",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Flink",
          "alias_type": "CANONICAL",
          "id": 314,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Apache Flink 1.20",
          "alias_type": "VERSION",
          "id": 318,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Apache Flink 1.x",
          "alias_type": "VERSION",
          "id": 317,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Flink 1.20",
          "alias_type": "VERSION",
          "id": 316,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Flink 1.x",
          "alias_type": "VERSION",
          "id": 315,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Apache Flink",
        "id": 120,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-flink",
        "sub_category_id": 94,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Stream Processing Systems",
            "id": 25,
            "rationale": "Technologies for processing event streams and near-real-time data flows. This includes stream transformations, windowing, stateful processing, and stream-to-warehouse delivery patterns.",
            "slug": "stream-processing-systems",
            "source": "db"
          },
          "input_skill": "Apache Flink",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Flink",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Apache Spark",
          "alias_type": "CANONICAL",
          "id": 2004,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "apache spark 3",
          "alias_type": "VERSION",
          "id": 2006,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark",
          "alias_type": "VERSION",
          "id": 2510,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark 3",
          "alias_type": "VERSION",
          "id": 2007,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark 3.x",
          "alias_type": "VERSION",
          "id": 2009,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "spark3",
          "alias_type": "VERSION",
          "id": 2008,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Apache Spark",
        "id": 1350,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Apache Spark",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Apache Spark",
      "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": [
    "Google Dataflow"
  ]
}

API 3 — final-role-output

{
  "chosen_role": {
    "display_name": "Streaming / Real-Time Data Engineer",
    "id": 149,
    "rationale": "Domain=Data Engineering \u0026 Analytics; The JD is centered on real-time stream processing, low-latency/high-throughput platform design, Kafka/Beam/Dataflow, and stateful event processing, which best fits a Streaming / Real-Time Data Engineer.",
    "role_archetype": null,
    "slug": "streaming-real-time-data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "Apache Beam",
      "tag": "in_db"
    },
    {
      "skill": "Google Dataflow",
      "tag": "new"
    },
    {
      "skill": "Apache Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Apache Flink",
      "tag": "in_db"
    },
    {
      "skill": "Apache Spark",
      "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": 149,
        "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": 149,
        "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": [
          {
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