Pipeline run
aa10c3d2-79ea-4ea4-a89f-58292bfeee88
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: 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.
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.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Soft Skills
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
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 |
Aliases — catalog
- Scala (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Airflow (CANONICAL) primary
- airflow 2 (VERSION)
- airflow-2 (VERSION)
- airflow2 (VERSION)
- airflow2.x (VERSION)
- apache airflow 2 (VERSION)
Context tags (catalog)
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) |
Aliases — catalog
- GCP (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- 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) |
Aliases — catalog
- Google Cloud Storage (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Object Storage Service
- Vendor
- 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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- BigQuery (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Data Warehouse Service
- Vendor
- 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 |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Cloud Platforms
- Sub-category
- general
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- HIPAA (CANONICAL) primary
Context tags (catalog)
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
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,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
"sentence": "Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.",
"similarity": 0.775
},
{
"kra_text": "Monitors pipeline health, SLA breach alerts, and job failure notifications, and performs root cause analysis for data pipeline incidents.",
"sentence": "Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.",
"similarity": 0.6902
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.",
"similarity": 0.6648
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.71,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.",
"similarity": 0.5524
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.",
"similarity": 0.5287
},
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.",
"similarity": 0.4819
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.521,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.",
"similarity": 0.6842
},
{
"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.",
"sentence": "Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.",
"similarity": 0.4331
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.",
"similarity": 0.4317
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.5163,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.",
"similarity": 0.5233
},
{
"kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
"sentence": "Design, implement, and optimize data pipelines using Spark with Scala, Airflow, and other relevant technologies.",
"similarity": 0.4883
},
{
"kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
"sentence": "Collaborate with product managers and data stewards to understand data requirements and translate them into efficient data processing workflows.",
"similarity": 0.4779
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4965,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
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"similarity": 0.5381
},
{
"kra_text": "Monitors AI feature behavior in production including response quality metrics, latency percentiles, token cost per request, and error rates.",
"sentence": "Monitor and troubleshoot data pipelines, ensuring minimal downtime and optimal performance.",
"similarity": 0.505
},
{
"kra_text": "Translates product requirements into AI-powered features by integrating large language models like GPT-4, Claude, or Gemini into application workflows via API.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.4857,
"slug": "ai-engineer",
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}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"Apache Spark",
"BigQuery",
"GCP",
"Google Cloud Storage",
"Scala"
],
"role_id": 2,
"score": 0.4167,
"slug": "data-engineer",
"total_count": 12
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Airflow",
"GCP",
"Scala"
],
"role_id": 3,
"score": 0.25,
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"total_count": 12
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"Airflow",
"GCP",
"Scala"
],
"role_id": 16,
"score": 0.25,
"slug": "ml-ops-engineer",
"total_count": 12
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"GCP",
"Google Cloud Storage"
],
"role_id": 9,
"score": 0.1667,
"slug": "cloud-architect",
"total_count": 12
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"GCP",
"HIPAA"
],
"role_id": 5,
"score": 0.1667,
"slug": "cybersecurity-engineer",
"total_count": 12
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 1.0,
"slug": "data-engineer",
"total_count": null
},
"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",
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}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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"alias_persisted": false,
"existing_alias_id": 2510,
"existing_alias_text": "spark",
"input_term": "Spark",
"matched_canonical": {
"category_id": 5,
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"id": 1350,
"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": {
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"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": {
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"display_name": "Airflow",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "airflow",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 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",
"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"
},
"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": {
"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"
},
"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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"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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},
{
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"source": "db"
},
{
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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"
},
{
"display_name": "Cyber Security Engineer",
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"slug": "cybersecurity-engineer",
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},
{
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"role_archetype": null,
"slug": "devops-engineer",
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},
{
"display_name": "Fullstack Developer",
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"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"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",
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},
{
"display_name": "Kotlin Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
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},
{
"display_name": "Node.js Backend Developer",
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"rationale": null,
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},
{
"display_name": "Python Backend Developer",
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"rationale": null,
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},
{
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},
{
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"slug": "ai-engineer",
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},
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"slug": "cloud-architect",
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},
{
"display_name": "Cloud Security Engineer",
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"slug": "cloud-security-engineer",
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},
{
"display_name": "Engineering Manager",
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"role_archetype": null,
"slug": "engineering-manager",
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},
{
"display_name": "Sitecore Dev",
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"slug": "sitecore-dev",
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}
],
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
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"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
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"source": "db"
},
"input_skill": "Spark",
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"roles_from_db": [
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Scala",
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"roles_from_db": [
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"slug": "data-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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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"
},
"input_skill": "Scala",
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{
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{
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"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.",
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},
"input_skill": "Airflow",
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"roles_from_db": [
{
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{
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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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{
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{
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{
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{
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},
{
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{
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]
},
{
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},
{
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},
{
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]
},
{
"dimension": {
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]
},
{
"dimension": {
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"source": "db"
},
"input_skill": "Google Cloud Storage",
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"roles_from_db": [
{
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"role_archetype": null,
"slug": "data-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
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"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.