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Pipeline run

aa10c3d2-79ea-4ea4-a89f-58292bfeee88

Pipeline LLM cost (USD)
API 1: $0.0033 API 2: $0.0002 API 3: $0.0000 Total: $0.0035

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data transformation and modeling
Build and optimize Spark/Scala and Airflow ETL pipelines on GCP to move healthcare data into a common data model, while monitoring jobs, fixing failures, and enforcing data quality and HIPAA/security requirements.
"Implement and maintain ETL processes to transform the healthcare data into a common data model."
Tech stack maturity
Mainstream Modern
The stack centers on established cloud data engineering tools and managed services like Airflow, Spark, BigQuery, GCP, and GCS, which are widely adopted but not bleeding-edge.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (12)
Data Engineer Spark Scala Airflow GCP Google Cloud Storage GCS BigQuery BQ Dataproc ETL HIPAA
Skill cluster (7 dimension groups, role-scoped)
Cloud Data Warehouses
BigQuery
Cloud Platforms
GCP
Cloud Storage and File Formats
Google Cloud Storage
Compliance and Security Frameworks
HIPAA
ETL and ELT Tooling
Spark
Programming Languages for Data Work
Scala
Cross-cutting / unaligned
Data Engineer Airflow GCS BQ Dataproc ETL
Show KRA description ↓
• Proven experience as a data engineer, with at least 5 years of experience in a similar role. • Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies. • Work with GCP services, including Google Cloud Storage (GCS), BigQuery (BQ), and Dataproc to manage and process big data. • Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows. • Ensure data quality, integrity, and security, with a strong emphasis on HIPAA compliance. • Implement and maintain ETL processes to transform the healthcare data into a common data model. • Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance. • Keep up-to-date with industry trends and best practices in data engineering and healthcare data. • Preferred experience in the healthcare domain, with a focus on healthcare data standards and regulations, including HIPAA. • Familiarity with common data models used in healthcare is a plus. • Strong problem-solving skills and the ability to work effectively in a collaborative team environment. • Excellent communication skills, both written and verbal.

Signals

Skill data-engineer
0.42
Alias data-engineer
1.00
KRA data-engineer
0.71

Post-classification

Centroidupdated · n=322
Alias collision log
New-role queue
New skills captured5
New KRA captured

Captured for admin review

Data Engineer primary Data Engineer pending
GCS primary Data Engineer pending
BQ primary Data Engineer pending
Dataproc primary Data Engineer pending
ETL primary Data Engineer pending
Status: completed Created: 2026-05-27T15:32:48.484612Z Updated: 2026-06-12T16:18:07.876388Z API 3 duration: 30702 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

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.42 does not contradict

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

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

Job description

• Proven experience as a data engineer, with at least 5 years of experience in a similar role.
• Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.
• Work with GCP services, including Google Cloud Storage (GCS), BigQuery (BQ), and Dataproc to manage and process big data.
• Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.
• Ensure data quality, integrity, and security, with a strong emphasis on HIPAA compliance.
• Implement and maintain ETL processes to transform the healthcare data into a common data model.
• Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.
• Keep up-to-date with industry trends and best practices in data engineering and healthcare data.
• Preferred experience in the healthcare domain, with a focus on healthcare data standards and regulations, including HIPAA.
• Familiarity with common data models used in healthcare is a plus.
• Strong problem-solving skills and the ability to work effectively in a collaborative team environment.
• Excellent communication skills, both written and verbal.

Skills from this JD

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

Data Engineer 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
Soft Skills
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

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

Aliases — catalog

  • Scala (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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, MLOps 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)
GCP Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GCP id=186 · gcp

Aliases — catalog

  • GCP (CANONICAL) primary

Context tags (catalog)

Anthos App Engine Artifact Registry BigQuery Cloud Build Cloud Composer Cloud Functions Cloud Logging Cloud Monitoring Cloud Run Cloud SQL Cloud Spanner Cloud Storage Compute Engine Dataflow GKE IAM Kubernetes Pub/Sub Service Accounts Stackdriver Terraform VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Google
License
other_open
Year introduced
2011
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GCP appears frequently in cloud/platform job descriptions and is a major hyperscaler alongside AWS/Azure, with broad enterprise adoption and active vendor investment.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

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

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Storage Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Google Cloud Storage id=171 · google-cloud-storage

Aliases — catalog

  • Google Cloud Storage (CANONICAL) primary

Context tags (catalog)

CMEK CORS Cloud Storage FUSE Cloud Storage buckets GCS IAM IAM permissions Uniform bucket-level access bucket bucket policy event notifications gsutil multipart upload object lifecycle resumable uploads retention policy service account signed URL signed URLs storage class storage classes uniform bucket-level access versioning

Stored enrichment (catalog DB)

Category
Service
Sub-category
Object Storage Service
Vendor
Google
License
proprietary
Year introduced
2010
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Broadly used object storage on GCP; appears frequently in cloud/data engineering JDs and is a standard managed service alongside S3/Azure Blob.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Storage and Data Services Catalog dimension db id 144

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Cloud Storage and File Formats Catalog dimension db id 35

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
GCS Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
BigQuery Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: BigQuery id=106 · bigquery

Aliases — catalog

  • BigQuery (CANONICAL) primary

Context tags (catalog)

Cloud Storage Dataflow ELT ETL GCP Google Cloud Platform Looker Pub/Sub SQL Standard SQL clustered tables data warehouse dbt partitioned tables service account

Stored enrichment (catalog DB)

Category
Service
Sub-category
Data Warehouse Service
Vendor
Google
License
proprietary
Year introduced
2011
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: BigQuery appears frequently in data/analytics job descriptions and is a core Google Cloud warehouse offering, with broad enterprise adoption and strong ecosystem support.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
118
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
BQ Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

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

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

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

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
HIPAA Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: HIPAA id=397 · hipaa

Aliases — catalog

  • HIPAA (CANONICAL) primary

Context tags (catalog)

BAA HIPAA training HITECH PHI administrative safeguards audit controls breach notification business associate business associates compliance covered entities covered entity data encryption data sharing ePHI electronic health information health information healthcare regulations medical records minimum necessary patient confidentiality patient consent physical safeguards privacy rule regulatory framework risk assessment security rule technical safeguards training requirements

Stored enrichment (catalog DB)

Category
Standard
Sub-category
Healthcare Privacy Standard
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: HIPAA is a baseline compliance requirement in U.S. healthcare JDs and vendor security questionnaires; it remains widely referenced rather than replaced by a successor standard.

Skill profile (library / DB)

Skill nature
STANDARD
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
274
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Compliance and Security Frameworks Catalog dimension db id 73

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Standards, Protocols & Compliance Catalog dimension db id 452

    Library dimension (catalog)

    Roles linked in library: Engineering Manager, Sitecore Dev

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Standards, Protocols & Compliance
standards-protocols-compliance
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
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension saved
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Airflow in_db
Workflow Orchestration for ML Pipelines
workflow-orchestration-for-ml-pipelines
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
GCP in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Storage in_db
Cloud Storage and Data Services
cloud-storage-and-data-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Google Cloud Storage in_db
Cloud Storage and File Formats
cloud-storage-and-file-formats
Existing dimension (library) · Role↔dimension saved
BigQuery in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
HIPAA in_db
Compliance and Security Frameworks
compliance-and-security-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
HIPAA in_db
Standards, Protocols & Compliance
standards-protocols-compliance
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Data Engineer | type=Soft Skills subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed GCS | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed BQ | type=Databases subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Dataproc | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
Experienceat least 5 years of experience in a similar role
DomainHealthcare
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "HealthTech",
        "MedTech"
      ],
      "domain": "Healthcare"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 5,
    "raw": "at least 5 years of experience in a similar role"
  },
  "job_locations": [],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Pipeline Engineer",
    "Big Data Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 11,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Proven experience as a data",
        "last_5_words": "both written and verbal."
      },
      "text": "\u2022 Proven experience as a data engineer, with at least 5 years of experience in a similar role.\n\u2022 Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.\n\u2022 Work with GCP services, including Google Cloud Storage (GCS), BigQuery (BQ), and Dataproc to manage and process big data.\n\u2022 Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.\n\u2022 Ensure data quality, integrity, and security, with a strong emphasis on HIPAA compliance.\n\u2022 Implement and maintain ETL processes to transform the healthcare data into a common data model.\n\u2022 Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.\n\u2022 Keep up-to-date with industry trends and best practices in data engineering and healthcare data.\n\u2022 Preferred experience in the healthcare domain, with a focus on healthcare data standards and regulations, including HIPAA.\n\u2022 Familiarity with common data models used in healthcare is a plus.\n\u2022 Strong problem-solving skills and the ability to work effectively in a collaborative team environment.\n\u2022 Excellent communication skills, both written and verbal.",
      "word_count": 174
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Data Engineer"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
      "skill_name": "Airflow"
    },
    {
      "is_primary": true,
      "skill_name": "GCP"
    },
    {
      "is_primary": true,
      "skill_name": "Google Cloud Storage"
    },
    {
      "is_primary": true,
      "skill_name": "GCS"
    },
    {
      "is_primary": true,
      "skill_name": "BigQuery"
    },
    {
      "is_primary": true,
      "skill_name": "BQ"
    },
    {
      "is_primary": true,
      "skill_name": "Dataproc"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "HIPAA"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Pipeline Engineer",
      "Big Data Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "HealthTech",
          "MedTech"
        ],
        "domain": "Healthcare"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 5,
      "raw": "at least 5 years of experience in a similar role"
    },
    "job_locations": [],
    "role": "Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Pipeline Engineer",
      "Big Data Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 11,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Proven experience as a data",
          "last_5_words": "both written and verbal."
        },
        "text": "\u2022 Proven experience as a data engineer, with at least 5 years of experience in a similar role.\n\u2022 Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.\n\u2022 Work with GCP services, including Google Cloud Storage (GCS), BigQuery (BQ), and Dataproc to manage and process big data.\n\u2022 Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.\n\u2022 Ensure data quality, integrity, and security, with a strong emphasis on HIPAA compliance.\n\u2022 Implement and maintain ETL processes to transform the healthcare data into a common data model.\n\u2022 Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.\n\u2022 Keep up-to-date with industry trends and best practices in data engineering and healthcare data.\n\u2022 Preferred experience in the healthcare domain, with a focus on healthcare data standards and regulations, including HIPAA.\n\u2022 Familiarity with common data models used in healthcare is a plus.\n\u2022 Strong problem-solving skills and the ability to work effectively in a collaborative team environment.\n\u2022 Excellent communication skills, both written and verbal.",
        "word_count": 174
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "aa10c3d2-79ea-4ea4-a89f-58292bfeee88",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
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      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
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            "similarity": 0.775
          },
          {
            "kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
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        ],
        "matched_count": null,
        "matched_skills": null,
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        "score": 0.71,
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      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
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          },
          {
            "kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
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      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
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            "kra_text": "Builds and maintains CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or CircleCI to automate build, test, security scanning, and deployment workflows.",
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          },
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            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
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      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
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          },
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            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
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          },
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            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
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        "score": 0.4965,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
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            "kra_text": "Monitors AI feature behavior in production including response quality metrics, latency percentiles, token cost per request, and error rates.",
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      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
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          "GCP",
          "Google Cloud Storage",
          "Scala"
        ],
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      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Airflow",
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          "Scala"
        ],
        "role_id": 3,
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        "total_count": 12
      },
      {
        "display_name": "MLOps Engineer",
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          "Airflow",
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      },
      {
        "display_name": "Cloud Architect",
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          "Google Cloud Storage"
        ],
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        "total_count": 12
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "GCP",
          "HIPAA"
        ],
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        "score": 0.1667,
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        "total_count": 12
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    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
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      "display_name": "Data Engineer",
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      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 1.0,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.42 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 322,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 15044,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Engineer",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15045,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "GCS",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15046,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "BQ",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15047,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Dataproc",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 15048,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
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    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2510,
      "existing_alias_text": "spark",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "apache-spark",
        "sub_category_id": 1021,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 272,
      "existing_alias_text": "Scala",
      "input_term": "Scala",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Scala",
        "id": 102,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "scala",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 405,
      "existing_alias_text": "GCP",
      "input_term": "GCP",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "GCP",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "gcp",
        "sub_category_id": 46,
        "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": 380,
      "existing_alias_text": "Google Cloud Storage",
      "input_term": "Google Cloud Storage",
      "matched_canonical": {
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        "display_name": "Google Cloud Storage",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "google-cloud-storage",
        "sub_category_id": 120,
        "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": 300,
      "existing_alias_text": "BigQuery",
      "input_term": "BigQuery",
      "matched_canonical": {
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        "display_name": "BigQuery",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "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": 713,
      "existing_alias_text": "HIPAA",
      "input_term": "HIPAA",
      "matched_canonical": {
        "category_id": 12,
        "display_name": "HIPAA",
        "id": 397,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "hipaa",
        "sub_category_id": 274,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
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      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
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    },
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      "slug": "ml-engineer",
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    },
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      "slug": "ml-ops-engineer",
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    },
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    },
    {
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      "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"
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      "display_name": "Cyber Security Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
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      "display_name": "DevOps Engineer",
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      "role_archetype": null,
      "slug": "devops-engineer",
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    },
    {
      "display_name": "Fullstack Developer",
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      "rationale": null,
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      "slug": "full-stack-engineer",
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    },
    {
      "display_name": "Go Backend Developer",
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      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
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      "display_name": "Node.js Backend Developer",
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      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
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    },
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      "display_name": "Python Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
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      "role_archetype": "Engineering",
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      "source": "db"
    },
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      "slug": "ai-engineer",
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    },
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      "display_name": "Cloud Architect",
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      "slug": "cloud-architect",
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    },
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      "display_name": "Cloud Security Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Engineering Manager",
      "id": 121,
      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
      "source": "db"
    },
    {
      "display_name": "Sitecore Dev",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "sitecore-dev",
      "source": "db"
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  ],
  "chosen_role": {
    "display_name": "Data Engineer",
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    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.42 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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        "source": "db"
      },
      "input_skill": "Spark",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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        }
      ]
    },
    {
      "dimension": {
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        "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.",
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        "source": "db"
      },
      "input_skill": "Scala",
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        }
      ]
    },
    {
      "dimension": {
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        "display_name": "Programming Languages for ML Systems",
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        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
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        "source": "db"
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      "input_skill": "Scala",
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      "roles_from_db": [
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      ]
    },
    {
      "dimension": {
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        "display_name": "Workflow Orchestration for ML Pipelines",
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        "source": "db"
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      ]
    },
    {
      "dimension": {
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        "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
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        "source": "db"
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        },
        {
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          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Security Posture Tools",
            "id": 64,
            "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
            "slug": "cloud-security-posture-tools",
            "source": "db"
          },
          "input_skill": "GCP",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "GCP",
      "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": "Google Cloud Storage",
          "alias_type": "CANONICAL",
          "id": 380,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "Google Cloud Storage",
        "id": 171,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "google-cloud-storage",
        "sub_category_id": 120,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Storage and Data Services",
            "id": 144,
            "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
            "slug": "cloud-storage-and-data-services",
            "source": "db"
          },
          "input_skill": "Google Cloud Storage",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Storage and File Formats",
            "id": 35,
            "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
            "slug": "cloud-storage-and-file-formats",
            "source": "db"
          },
          "input_skill": "Google Cloud Storage",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Google Cloud Storage",
      "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": "GCS",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "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": "gcs",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "BigQuery",
          "alias_type": "CANONICAL",
          "id": 300,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "BigQuery",
        "id": 106,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CLOUD_SERVICE",
        "slug": "bigquery",
        "sub_category_id": 118,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Data Warehouses",
            "id": 22,
            "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
            "slug": "cloud-data-warehouses",
            "source": "db"
          },
          "input_skill": "BigQuery",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "BigQuery",
      "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": "BQ",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "bq",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Dataproc",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Cloud Platforms",
          "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": "dataproc",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "CONCEPT",
          "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": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "HIPAA",
          "alias_type": "CANONICAL",
          "id": 713,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 12,
        "display_name": "HIPAA",
        "id": 397,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "STANDARD",
        "slug": "hipaa",
        "sub_category_id": 274,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Compliance and Security Frameworks",
            "id": 73,
            "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
            "slug": "compliance-and-security-frameworks",
            "source": "db"
          },
          "input_skill": "HIPAA",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Standards, Protocols \u0026 Compliance",
            "id": 452,
            "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
            "slug": "standards-protocols-compliance",
            "source": "db"
          },
          "input_skill": "HIPAA",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            },
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "HIPAA",
      "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": [
    "Data Engineer",
    "GCS",
    "BQ",
    "Dataproc",
    "ETL"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.42 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Data Engineer",
      "tag": "new"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "GCP",
      "tag": "in_db"
    },
    {
      "skill": "Google Cloud Storage",
      "tag": "in_db"
    },
    {
      "skill": "GCS",
      "tag": "new"
    },
    {
      "skill": "BigQuery",
      "tag": "in_db"
    },
    {
      "skill": "BQ",
      "tag": "new"
    },
    {
      "skill": "Dataproc",
      "tag": "new"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "HIPAA",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Scala",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 102,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Scala",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 102,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "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"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-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": "Cloud Platforms",
          "id": 20,
          "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
          "slug": "cloud-platforms",
          "source": "db"
        },
        "dimension_id": 20,
        "input_skill": "GCP",
        "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": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Go Backend Developer",
            "id": 81,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "go-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platforms for AI Deployment",
          "id": 211,
          "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
          "slug": "cloud-platforms-for-ai-deployment",
          "source": "db"
        },
        "dimension_id": 211,
        "input_skill": "GCP",
        "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": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Platforms",
          "id": 131,
          "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
          "slug": "cloud-provider-platforms",
          "source": "db"
        },
        "dimension_id": 131,
        "input_skill": "GCP",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          },
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Posture Tools",
          "id": 64,
          "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
          "slug": "cloud-security-posture-tools",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "GCP",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and Data Services",
          "id": 144,
          "rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
          "slug": "cloud-storage-and-data-services",
          "source": "db"
        },
        "dimension_id": 144,
        "input_skill": "Google Cloud Storage",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Storage and File Formats",
          "id": 35,
          "rationale": "Object storage and data file formats used as the physical substrate for data movement and lake-style analytics. Data engineers need these to manage landing zones, partitioned datasets, and efficient interchange.",
          "slug": "cloud-storage-and-file-formats",
          "source": "db"
        },
        "dimension_id": 35,
        "input_skill": "Google Cloud Storage",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 171,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "BigQuery",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 106,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Compliance and Security Frameworks",
          "id": 73,
          "rationale": "Formal control frameworks and regulatory standards used to assess and document security posture. This dimension is coherent because the role translates technical controls into auditable requirements and evidence.",
          "slug": "compliance-and-security-frameworks",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "HIPAA",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          },
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 397,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Standards, Protocols \u0026 Compliance",
          "id": 452,
          "rationale": "Ensure teams adhere to industry standards, security protocols, and regulatory compliance requirements.",
          "slug": "standards-protocols-compliance",
          "source": "db"
        },
        "dimension_id": 452,
        "input_skill": "HIPAA",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          },
          {
            "display_name": "Sitecore Dev",
            "id": 233,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "sitecore-dev",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 397,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "aa10c3d2-79ea-4ea4-a89f-58292bfeee88"
}

LLM Calls

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

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