← Back to history

Pipeline run

d1284c9b-3959-4f53-b9f9-09085e1072b9

Pipeline LLM cost (USD)
API 1: $0.0028 API 2: $0.0316 API 3: $0.0000 Total: $0.0344

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD role baseline loaded sources · ai_index: role_baseline · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Build and operate high-scale batch and streaming data pipelines and backend services for reporting/analytics, with strong focus on data modeling, quality checks, and production reliability using Spark, Flink, Kafka, and Airflow.
"Design architecture and development of high-scale data pipelines and backend services for data processing and storage."
Tech stack maturity
Modern Cloud Native
The stack centers on widely adopted modern data engineering tools like Airflow, Spark, Flink, and Kafka, which are commonly used in cloud-native data platforms.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): ML
Evidence — skills matched in JD (4)
Spark Flink Kafka Airflow
Skill cluster (3 dimension groups, role-scoped)
ETL and ELT Tooling
Spark Flink
Messaging and Event Streaming
Kafka
Cross-cutting / unaligned
Airflow
Show KRA description ↓
You will play a critical role in expanding and optimizing our data platform and reporting capabilities. You will work on the development of a scalable high-impact high volume data systems powering reporting and analytics for our advertising business. This is a multi-faceted role requiring expertise in backend service development, streaming and batch processing, and operational excellence. Your responsibilities will include: Design architecture and development of high-scale data pipelines and backend services for data processing and storage. Work closely with product teams to understand data needs and translate them into reliable performant systems. Design and implement batch and real-time processing pipelines using modern big data tools (e.g., Spark, Flink, Kafka, Airflow). Drive data modeling best practices to ensure consistent extensible data definitions across the organization. Ensure data quality, correctness, and completeness through robust monitoring, validation, and testing strategies. Mentor junior engineers, foster engineering excellence, and help shape technical direction across the broader organization. Partner with infrastructure and platform teams to ensure systems are cost-efficient, observable, and resilient at scale. Develop and enforce data engineering security, data quality standards through automation. Participate in supporting platform 24X7. Be passionate about growing a team - hire and mentor engineers.

Signals

Skill backend-engineer
0.25
Alias data-engineer
0.61
KRA data-engineer
0.46

Post-classification

Centroidupdated · n=15
Alias collision log
New-role queue
New skills captured2
New KRA captured

Captured for admin review

Spark primary Data Engineer pending
Flink primary Data Engineer pending
Status: completed Created: 2026-05-19T06:08:59.492114Z Updated: 2026-05-19T06:10:46.239444Z API 3 duration: 3213 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

CASE A

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

The primary skills require expertise in data processing and orchestration tools, fitting the Data Engineer role well.

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

2
New skills
4
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Data Platform Engineer

What You ll Do You will play a critical role in expanding and optimizing our data platform and reporting capabilities You will work on the development of a scalable high-impact high volume data systems powering reporting and analytics for our advertising business This is a multi-faceted role requiring expertise in backend service development streaming and batch processing and operational excellence Your responsibilities will include Design architecture and development of high-scale data pipelines and backend services for data processing and storage Work closely with product teams to understand data needs and translate them into reliable performant systems Design and implement batch and real-time processing pipelines using modern big data tools e g Spark Flink Kafka Airflow Drive data modeling best practices to ensure consistent extensible data definitions across the organization Ensure data quality correctness and completeness through robust monitoring validation and testing strategies Mentor junior engineers foster engineering excellence and help shape technical direction across the broader organization Partner with infrastructure and platform teams to ensure systems are cost-efficient observable and resilient at scale Develop and enforce data engineering security data quality standards through automation Participate in supporting platform 24X7 Be passionate about growing a team - hire and mentor engineers What to Bring Bachelor s degree in computer science or similar discipline 11 - 15 years of experience in software engineering with a strong background in data-intensive systems Deep experience with distributed data processing frameworks e g Apache Spark Beam Flink Proficiency in one or more programming languages such as Java Python or Go Strong understanding of data modelling ETL best practices and big data architecture Experience building reporting pipelines or systems that support forecasting attribution reach frequency or audience measurement Exposure to ML-based forecasting systems or time-series modelling Expertise in building and managing large volume stream or batch processing platform is a must Experience working with data warehousing solutions like Databricks Practical knowledge of with CI CD pipelines preferably GitHub Actions Workflows Experience with Microservice Architecture principles and implementations Practical knowledge of containerization and orchestration platforms like Docker and EKS Experience with cloud services especially AWS is highly desirable Strong interpersonal communication and presentation skills

Skills from this JD

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

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

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.95

Apache Spark appears in many data engineering and analytics job descriptions and remains a standard big-data processing stack alongside Databricks and Hadoop ecosystems.

Vendor & license

Apache Software Foundation ·apache_2 ·since 2010 (0.95)

Context keywords
Hadoop RDD DataFrame Spark SQL MLlib Streaming PySpark Cluster Resilient Distributed Dataset GraphX Apache ETL Big Data Scala Java
Ambiguity low

“Spark” in JDs typically refers to Apache Spark for data processing; other common meanings are less likely in this engineering context.

Versioning

Versioned 3.5

{
  "apache spark 3": "3",
  "apache spark 3.5": "3.5",
  "spark 3": "3",
  "spark 3.5": "3.5",
  "spark 3.x": "3",
  "spark3": "3",
  "spark3.5": "3.5"
}
Type assignment

Framework ·data_processing_framework confidence 0.93

Spark is fundamentally a distributed application framework that users build data-processing jobs inside, not a standalone tool they merely operate.

Derived legacy fields
Category
Framework
Sub-category
data_processing_framework
Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Distributed Data Processing

    Pipeline tentative id

    Batch and streaming data processing frameworks used to transform large datasets across clusters. Spark belongs here because it is a core engine for distributed ETL, analytics, and scalable data pipelines.

  • Big Data Analytics Engines

    Pipeline tentative id

    Large-scale analytics engines used to query, transform, and aggregate data on distributed storage. Spark fits because it is commonly used as the execution layer for big data batch analytics and interactive processing.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Systems Programming
d_init_02
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flink Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.84

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

Vendor & license

Apache Software Foundation ·apache_2 ·since 2014 (0.95)

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

“Flink” in JDs typically refers specifically to Apache Flink (stream/batch processing), not another catalog skill with a similar name.

Versioning

Versioned 1.20

{
  "Apache Flink": "1.20",
  "Flink 1.20": "1.20",
  "Flink 1.20.x": "1.20",
  "Flink 1.x": "1.20"
}
Type assignment

Framework ·stream_processing_framework confidence 0.90

Flink is fundamentally a structured distributed processing framework that developers build stream and batch applications on, rather than a standalone tool they merely operate.

Derived legacy fields
Category
Framework
Sub-category
stream_processing_framework
Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Stream Processing Frameworks

    Reuses catalog slug

    Frameworks used to build and operate batch and streaming data pipelines. Flink belongs here because it is a core engine for stateful stream processing, event-time handling, and real-time ETL in data platforms.

  • Distributed Stream Processing

    Pipeline tentative id

    Distributed engines and concepts for processing high-volume event streams with state, fault tolerance, and low latency. Flink fits here because it is widely used as a distributed runtime for continuous data pipelines and real-time analytics.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
New skill saved · Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
47
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 Engineer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Airflow Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Airflow id=265 · airflow

Aliases — catalog

  • Airflow (CANONICAL) primary
  • airflow 2 (VERSION)
  • airflow-2 (VERSION)
  • airflow2 (VERSION)
  • airflow2.x (VERSION)
  • apache airflow 2 (VERSION)

Context tags (catalog)

Apache Celery CeleryExecutor DAG ETL Executor Jinja templating Python SLA Sensors UI XCom backfill connections data pipeline executor hooks logging monitoring operators plugins scheduler task dependencies task instance variables

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Workflow Orchestration Tool
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2014
Confidence
0.95
Version strategy
SEPARATE_ENTITY
Version tag
2.x

Maturity reasoning: Apache Airflow appears in many data engineering job postings and is a common orchestration choice in production stacks; its GitHub activity and ecosystem remain strong, with no vendor sunset or clear replacement dominating JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
130
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Workflow Orchestration for ML Pipelines Catalog dimension db id 54

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
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
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension saved
Airflow in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
Systems Programming
d_init_02
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flink in_db
ETL and ELT Tooling
etl-and-elt-tooling
New skill saved · Existing dimension (library) · Role↔dimension saved
Flink in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_added Spark 1348
canonical_skill_added Flink 1349
dimension_skill_link Spark ↔ React Frontend Development 96
dimension_skill_link Spark ↔ Systems Programming 166
dimension_skill_link Flink ↔ ETL and ELT Tooling 24
dimension_skill_link Flink ↔ React Frontend Development 96
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Platform Engineer
Experience11 - 15 years of experience
DomainOther
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE - Computer Science (or similar)",
      "raw": "Bachelor s degree in computer science or similar discipline",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 15,
    "min": 11,
    "raw": "11 - 15 years of experience"
  },
  "job_locations": [],
  "role": "Data Platform Engineer",
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 10,
      "heading": "What You ll Do",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "You will play a critical",
        "last_5_words": "hire and mentor engineers."
      },
      "text": "You will play a critical role in expanding and optimizing our data platform and reporting capabilities. You will work on the development of a scalable high-impact high volume data systems powering reporting and analytics for our advertising business. This is a multi-faceted role requiring expertise in backend service development, streaming and batch processing, and operational excellence. Your responsibilities will include:\n\nDesign architecture and development of high-scale data pipelines and backend services for data processing and storage.\nWork closely with product teams to understand data needs and translate them into reliable performant systems.\nDesign and implement batch and real-time processing pipelines using modern big data tools (e.g., Spark, Flink, Kafka, Airflow).\nDrive data modeling best practices to ensure consistent extensible data definitions across the organization.\nEnsure data quality, correctness, and completeness through robust monitoring, validation, and testing strategies.\nMentor junior engineers, foster engineering excellence, and help shape technical direction across the broader organization.\nPartner with infrastructure and platform teams to ensure systems are cost-efficient, observable, and resilient at scale.\nDevelop and enforce data engineering security, data quality standards through automation.\nParticipate in supporting platform 24X7.\nBe passionate about growing a team - hire and mentor engineers.",
      "word_count": 263
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Flink"
    },
    {
      "is_primary": true,
      "skill_name": "Kafka"
    },
    {
      "is_primary": true,
      "skill_name": "Airflow"
    }
  ],
  "jd_role": {
    "display_name": "Data Platform Engineer",
    "rationale": null,
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE - Computer Science (or similar)",
        "raw": "Bachelor s degree in computer science or similar discipline",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": 15,
      "min": 11,
      "raw": "11 - 15 years of experience"
    },
    "job_locations": [],
    "role": "Data Platform Engineer",
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 10,
        "heading": "What You ll Do",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "You will play a critical",
          "last_5_words": "hire and mentor engineers."
        },
        "text": "You will play a critical role in expanding and optimizing our data platform and reporting capabilities. You will work on the development of a scalable high-impact high volume data systems powering reporting and analytics for our advertising business. This is a multi-faceted role requiring expertise in backend service development, streaming and batch processing, and operational excellence. Your responsibilities will include:\n\nDesign architecture and development of high-scale data pipelines and backend services for data processing and storage.\nWork closely with product teams to understand data needs and translate them into reliable performant systems.\nDesign and implement batch and real-time processing pipelines using modern big data tools (e.g., Spark, Flink, Kafka, Airflow).\nDrive data modeling best practices to ensure consistent extensible data definitions across the organization.\nEnsure data quality, correctness, and completeness through robust monitoring, validation, and testing strategies.\nMentor junior engineers, foster engineering excellence, and help shape technical direction across the broader organization.\nPartner with infrastructure and platform teams to ensure systems are cost-efficient, observable, and resilient at scale.\nDevelop and enforce data engineering security, data quality standards through automation.\nParticipate in supporting platform 24X7.\nBe passionate about growing a team - hire and mentor engineers.",
        "word_count": 263
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "d1284c9b-3959-4f53-b9f9-09085e1072b9",
  "stage3_signals": {
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "matched_count": null,
        "role_id": 2,
        "score": 0.6087,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.3462,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Frontend Engineer",
        "matched_count": null,
        "role_id": 7,
        "score": 0.3462,
        "slug": "frontend-engineer",
        "total_count": null
      },
      {
        "display_name": "AR/VR Engineer",
        "matched_count": null,
        "role_id": 8,
        "score": 0.3462,
        "slug": "ar-vr-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "matched_count": null,
        "role_id": 13,
        "score": 0.3462,
        "slug": "ai-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "matched_count": null,
        "role_id": 2,
        "score": 0.4618,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "Android Engineer",
        "matched_count": null,
        "role_id": 4,
        "score": 0.4519,
        "slug": "android-engineer",
        "total_count": null
      },
      {
        "display_name": "Backend Engineer",
        "matched_count": null,
        "role_id": 1,
        "score": 0.4271,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "AR/VR Engineer",
        "matched_count": null,
        "role_id": 8,
        "score": 0.4137,
        "slug": "ar-vr-engineer",
        "total_count": null
      },
      {
        "display_name": "Cloud Architect",
        "matched_count": null,
        "role_id": 9,
        "score": 0.4114,
        "slug": "cloud-architect",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Backend Engineer",
        "matched_count": 1,
        "role_id": 1,
        "score": 0.25,
        "slug": "backend-engineer",
        "total_count": 4
      },
      {
        "display_name": "Data Engineer",
        "matched_count": 1,
        "role_id": 2,
        "score": 0.25,
        "slug": "data-engineer",
        "total_count": 4
      },
      {
        "display_name": "ML Engineer",
        "matched_count": 1,
        "role_id": 3,
        "score": 0.25,
        "slug": "ml-engineer",
        "total_count": 4
      }
    ],
    "stage35_ran": false
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "Data Engineer",
      "matched_count": null,
      "role_id": 2,
      "score": 1.0,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 0.4618,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "queued": false,
    "reasoning": "Stage 1 title \u0027Data Engineer\u0027 (embedding match, sim 0.79); KRA agrees (0.46)"
  },
  "stage5_updates": {
    "centroid_n_after": 15,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 1086,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Spark",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1087,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Flink",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 173,
      "existing_alias_text": "Kafka",
      "input_term": "Kafka",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kafka",
        "sub_category_id": 47,
        "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": 526,
      "existing_alias_text": "Airflow",
      "input_term": "Airflow",
      "matched_canonical": {
        "category_id": 13,
        "display_name": "Airflow",
        "id": 265,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "airflow",
        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Backend Engineer",
      "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"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "The primary skills require expertise in data processing and orchestration tools, fitting the Data Engineer role well.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "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": "Kafka",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Workflow Orchestration for ML Pipelines",
        "id": 54,
        "rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
        "slug": "workflow-orchestration-for-ml-pipelines",
        "source": "db"
      },
      "input_skill": "Airflow",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "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": "Spark",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Systems Programming",
        "id": 166,
        "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
        "slug": "d_init_02",
        "source": "db"
      },
      "input_skill": "Spark",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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": "Flink",
      "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": "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": "Flink",
      "llm_role": null,
      "roles_from_db": []
    }
  ],
  "input_final_skills": [
    "Spark",
    "Flink",
    "Kafka",
    "Airflow"
  ],
  "input_llm_skills": [
    "Spark",
    "Flink",
    "Kafka",
    "Airflow"
  ],
  "new_aliases_persisted": 0,
  "run_id": "d1284c9b-3959-4f53-b9f9-09085e1072b9",
  "skills_detail": [
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Spark",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Systems Programming",
            "id": 166,
            "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
            "slug": "d_init_02",
            "source": "db"
          },
          "input_skill": "Spark",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Spark",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Framework",
          "skill_nature": "FRAMEWORK",
          "sub_category": "data_processing_framework",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "SEPARATE_ENTITY",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cSpark\u201d in JDs typically refers to Apache Spark for data processing; other common meanings are less likely in this engineering context."
          },
          "context_keywords": {
            "context_keywords": [
              "Hadoop",
              "RDD",
              "DataFrame",
              "Spark SQL",
              "MLlib",
              "Streaming",
              "PySpark",
              "Cluster",
              "Resilient Distributed Dataset",
              "GraphX",
              "Apache",
              "ETL",
              "Big Data",
              "Scala",
              "Java"
            ]
          },
          "maturity": {
            "confidence": 0.95,
            "maturity": "well_known",
            "reasoning": "Apache Spark appears in many data engineering and analytics job descriptions and remains a standard big-data processing stack alongside Databricks and Hadoop ecosystems."
          },
          "skill_id": "spark",
          "vendor_license": {
            "confidence": 0.95,
            "license": "apache_2",
            "vendor": "Apache Software Foundation",
            "year_introduced": 2010
          },
          "versioning": {
            "current_version": "3.5",
            "version_aliases": {
              "apache spark 3": "3",
              "apache spark 3.5": "3.5",
              "spark 3": "3",
              "spark 3.5": "3.5",
              "spark 3.x": "3",
              "spark3": "3",
              "spark3.5": "3.5"
            },
            "versioned": true
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Batch and streaming data processing frameworks used to transform large datasets across clusters. Spark belongs here because it is a core engine for distributed ETL, analytics, and scalable data pipelines.",
            "exemplar_skills": [
              "Spark",
              "Spark SQL",
              "DataFrame API",
              "RDDs",
              "Structured Streaming",
              "PySpark",
              "shuffle optimization",
              "partition tuning"
            ],
            "in_scope": "Spark, Spark SQL, DataFrame API, RDDs, Structured Streaming, cluster execution, shuffle, partitioning, joins, window functions, broadcast joins, UDFs, PySpark",
            "name": "Distributed Data Processing",
            "out_of_scope": "Workflow orchestration tools like Airflow, connector-first ETL products, and warehouse modeling belong to ETL and ELT Tooling; low-level JVM or Python language syntax belongs to Programming Languages and Scripting",
            "overlap_flags": [
              {
                "reason": "Spark is often used inside ETL/ELT pipelines, but this dimension is about the processing engine rather than orchestration or packaged ingestion tools.",
                "with_dim_id": "etl-and-elt-tooling",
                "with_dim_name": null,
                "with_role": "Data Engineer"
              },
              {
                "reason": "Spark tuning frequently involves performance work, but the primary focus here is distributed data processing semantics and APIs.",
                "with_dim_id": "performance-and-scalability-tuning",
                "with_dim_name": null,
                "with_role": "Backend Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          },
          {
            "description": "Large-scale analytics engines used to query, transform, and aggregate data on distributed storage. Spark fits because it is commonly used as the execution layer for big data batch analytics and interactive processing.",
            "exemplar_skills": [
              "Spark",
              "Spark SQL",
              "distributed aggregations",
              "large-scale joins",
              "parquet processing",
              "batch analytics",
              "interactive queries"
            ],
            "in_scope": "Spark, Spark SQL, distributed aggregations, large-scale joins, parquet processing, cluster-based analytics, notebook-driven exploration, batch analytics, interactive queries",
            "name": "Big Data Analytics Engines",
            "out_of_scope": "Standalone BI dashboards and semantic layers belong to BI and Visualization Tools; storage systems like data lakes and warehouses belong to Cloud Storage and Data Services",
            "overlap_flags": [
              {
                "reason": "Spark commonly reads from and writes to cloud data stores, but the engine itself is the analytics layer rather than the storage layer.",
                "with_dim_id": "cloud-storage-and-data-services",
                "with_dim_name": null,
                "with_role": "Cloud Architect"
              }
            ],
            "tentative_id": "d_init_02"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Spark",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "d_init_02"
          ],
          "skill_id": "spark"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "databricks",
            "aws",
            "azure",
            "kubernetes",
            "jvm",
            "sqlite",
            "git",
            "github"
          ],
          "requires": [],
          "skill_id": "spark",
          "suppress_on_match": []
        },
        "skill_id": "spark",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.93,
          "name": "Spark",
          "reasoning": "Spark is fundamentally a distributed application framework that users build data-processing jobs inside, not a standalone tool they merely operate.",
          "skill_id": "spark",
          "subtype": "data_processing_framework",
          "type": "Framework"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "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": "Flink",
          "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": "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": "Flink",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Flink",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Framework",
          "skill_nature": "FRAMEWORK",
          "sub_category": "stream_processing_framework",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "SEPARATE_ENTITY",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cFlink\u201d in JDs typically refers specifically to Apache Flink (stream/batch processing), not another catalog skill with a similar name."
          },
          "context_keywords": {
            "context_keywords": [
              "Kafka",
              "streaming",
              "data pipeline",
              "event time",
              "windowing",
              "stateful processing",
              "checkpointing",
              "Flink SQL",
              "Apache Beam",
              "dataflow",
              "real-time analytics",
              "backpressure",
              "flink-connector",
              "flink-ml",
              "flink-runtime"
            ]
          },
          "maturity": {
            "confidence": 0.84,
            "maturity": "well_known",
            "reasoning": "Apache Flink appears in many data/streaming job postings and is a standard choice alongside Kafka/Spark for real-time ETL; its GitHub and vendor ecosystem remain active, indicating broad adoption."
          },
          "skill_id": "flink",
          "vendor_license": {
            "confidence": 0.95,
            "license": "apache_2",
            "vendor": "Apache Software Foundation",
            "year_introduced": 2014
          },
          "versioning": {
            "current_version": "1.20",
            "version_aliases": {
              "Apache Flink": "1.20",
              "Flink 1.20": "1.20",
              "Flink 1.20.x": "1.20",
              "Flink 1.x": "1.20"
            },
            "versioned": true
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Frameworks used to build and operate batch and streaming data pipelines. Flink belongs here because it is a core engine for stateful stream processing, event-time handling, and real-time ETL in data platforms.",
            "exemplar_skills": [
              "Flink",
              "Apache Flink",
              "stream processing",
              "event-time processing",
              "windowing",
              "checkpointing",
              "watermarks",
              "stateful stream processing",
              "real-time ETL"
            ],
            "in_scope": "Flink, Apache Flink, stream processing jobs, event-time processing, windowing, stateful transformations, checkpointing, watermarks, connectors, sink and source integration, real-time ETL, batch processing with Flink",
            "name": "Stream Processing Frameworks",
            "out_of_scope": "SQL-only transformations and warehouse modeling, which belong to analytics engineering; low-level distributed systems internals, which belong to platform architecture; orchestration of scheduled workflows, which belongs to workflow orchestration tools",
            "overlap_flags": [
              {
                "reason": "Flink uses parallel execution and coordination concepts, but the skill is primarily about a data processing framework rather than general concurrency patterns.",
                "with_dim_id": "concurrency-and-parallel-processing",
                "with_dim_name": null,
                "with_role": "Backend Engineer"
              },
              {
                "reason": "Flink tuning often involves throughput, latency, and state backend optimization, which can overlap with general performance work.",
                "with_dim_id": "performance-and-scalability-tuning",
                "with_dim_name": null,
                "with_role": "Backend Engineer"
              }
            ],
            "tentative_id": "etl-and-elt-tooling"
          },
          {
            "description": "Distributed engines and concepts for processing high-volume event streams with state, fault tolerance, and low latency. Flink fits here because it is widely used as a distributed runtime for continuous data pipelines and real-time analytics.",
            "exemplar_skills": [
              "Flink",
              "distributed stream processing",
              "exactly-once processing",
              "event-time semantics",
              "backpressure",
              "checkpointing",
              "stateful operators",
              "watermarks"
            ],
            "in_scope": "Flink, distributed stream processing, event-driven pipelines, stateful operators, fault tolerance, exactly-once processing, event-time semantics, watermarks, backpressure, checkpointing, parallel stream execution",
            "name": "Distributed Stream Processing",
            "out_of_scope": "General-purpose message brokers and queues, which belong to messaging infrastructure; warehouse ELT tools, which belong to ETL and ELT tooling; application-level concurrency primitives, which belong to programming and parallel processing",
            "overlap_flags": [
              {
                "reason": "Many teams use Flink as an ETL/ELT engine, so the boundary between pipeline tooling and stream-processing architecture can be blurred.",
                "with_dim_id": "etl-and-elt-tooling",
                "with_dim_name": null,
                "with_role": "Data Engineer"
              },
              {
                "reason": "Flink\u0027s execution model relies on parallelism, but this dimension focuses on distributed dataflow rather than generic concurrency techniques.",
                "with_dim_id": "concurrency-and-parallel-processing",
                "with_dim_name": null,
                "with_role": "Backend Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Flink",
          "placement_confidence": 0.92,
          "primary_dimension": "etl-and-elt-tooling",
          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "d_init_01"
          ],
          "skill_id": "flink"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "databricks",
            "splunk",
            "nosql",
            "scrum",
            "mlops",
            "langchain",
            "kotlin"
          ],
          "requires": [],
          "skill_id": "flink",
          "suppress_on_match": []
        },
        "skill_id": "flink",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "Flink",
          "reasoning": "Flink is fundamentally a structured distributed processing framework that developers build stream and batch applications on, rather than a standalone tool they merely operate.",
          "skill_id": "flink",
          "subtype": "stream_processing_framework",
          "type": "Framework"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Kafka",
          "alias_type": "CANONICAL",
          "id": 173,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Kafka",
        "id": 36,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kafka",
        "sub_category_id": 47,
        "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": "Kafka",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "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": "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": "Airflow",
          "alias_type": "CANONICAL",
          "id": 526,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Airflow",
        "id": 265,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "airflow",
        "sub_category_id": 130,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Workflow Orchestration for ML Pipelines",
            "id": 54,
            "rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
            "slug": "workflow-orchestration-for-ml-pipelines",
            "source": "db"
          },
          "input_skill": "Airflow",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Airflow",
      "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": [
    "Spark",
    "Flink"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "The primary skills require expertise in data processing and orchestration tools, fitting the Data Engineer role well.",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Spark",
      "tag": "new"
    },
    {
      "skill": "Flink",
      "tag": "new"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 8,
        "input_skill": "Kafka",
        "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": "Backend Engineer",
            "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 36,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Workflow Orchestration for ML Pipelines",
          "id": 54,
          "rationale": "Workflow engines used to coordinate training, evaluation, deployment, and retraining jobs. This cluster covers dependencies, retries, scheduling, and pipeline composition for ML lifecycle automation.",
          "slug": "workflow-orchestration-for-ml-pipelines",
          "source": "db"
        },
        "dimension_id": 54,
        "input_skill": "Airflow",
        "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 265,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1348,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Systems Programming",
          "id": 166,
          "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 166,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1348,
        "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": "Flink",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "New skill saved \u00b7 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": 1349,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "Flink",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1349,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 2,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 4,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "d1284c9b-3959-4f53-b9f9-09085e1072b9"
}

LLM Calls

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

Loading…