Pipeline run
eea7a704-e4e0-444b-b423-d0187a8162c5
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 0.50 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Immediate Requirement and Full time Employment - Must have experience withNifi and an overall experience of 3 years Technical Skills: • BigData skills like Kafka and NiFI, AWS Services like S3 • ETL workflow development and building ETL pipelines using NiFi • Experience in the field of Big Data Technologies, software programming and developing • Knowledge of ETL framework with Snowflake cloud technology • Experience with big data tools Spark, Hadoop Kafka would be good to have • Hands on experience in Microsoft Azure Data Factory Azure Logic apps service Data Bricks • Experience integrating different data sources • Participate with team and do requirement analysis and convert into technical documentation • Identify the performance bottle-necks and resolve the same Soft skills: • Good communication(Written and Verbal) and interpersonal skills • The ability to plan work and meet deadlines • Accuracy and attention to detail • Should be able to work in a team or as an individual contributor • To coordinate with developers / other architects / other stakeholders and cross functional teams from organization and customer side. Key skillset: Nifi, Data Management, Data Analysis Experience: 3+ years If you have the required skill sets please apply or send us your profile at - Apply@algoodmen.com or Amy@algoodmen.com
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Kafka (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Datastore
- Sub-category
- Event Stream Store
- Vendor
- Confluent
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 3
- Sub-category id
- 3533
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Asynchronous Messaging and Event Streaming Catalog dimension db id 297
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer
-
Messaging and Background Jobs Catalog dimension db id 291
Library dimension (catalog)
Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer
-
Messaging and Event Streaming Catalog dimension db id 8
Library dimension (catalog)
Roles linked in library: Backend Developer, Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Amazon
- License
- other_open
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.
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
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
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) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Amazon S3 (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Service
- Sub-category
- Object Storage Service
- Vendor
- Amazon Web Services
- License
- proprietary
- Year introduced
- 2006
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Amazon S3 is a standard cloud storage service widely listed in job descriptions and core AWS certifications; it remains a default object-storage choice rather than a niche or sunset product.
Skill profile (library / DB)
- Skill nature
- CLOUD_SERVICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 120
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Storage and Data Services Catalog dimension db id 144
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Cloud Storage and File Formats Catalog dimension db id 35
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- Snowflake (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Cloud Platform
- Vendor
- Snowflake Inc.
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 113
- 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 |
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
- Hadoop (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2006
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 91
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
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
- Microsoft Azure (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Microsoft
- License
- other_open
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Azure appears in large volumes of cloud/DevOps job descriptions and is a core hyperscaler alongside AWS/GCP; Microsoft’s continued product investment and broad enterprise adoption signal mainstream demand.
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 & Hosting Providers Catalog dimension db id 414
Library dimension (catalog)
Roles linked in library: PHP Backend Developer
-
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud & Hosting Providers
cloud-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | 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
- Data Integration
- 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
- Cloud Platforms
- Sub-category
- Integration Services
- 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
- Cloud Platforms
- Sub-category
- Data Analytics
- Skill nature
- PLATFORM
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Kafka | in_db |
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Background Jobs
messaging-and-background-jobs
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Amazon S3 | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Amazon S3 | in_db |
Cloud Storage and File Formats
cloud-storage-and-file-formats
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Snowflake | in_db |
Cloud Data Warehouses
cloud-data-warehouses
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Apache Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hadoop | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Microsoft Azure | in_db |
Cloud & Hosting Providers
cloud-hosting-providers
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Microsoft Azure | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Apache NiFi | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Azure Data Factory | type=Cloud Platforms subtype=Data Integration nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Logic Apps | type=Cloud Platforms subtype=Integration Services nature=PLATFORM lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Azure Databricks | type=Cloud Platforms subtype=Data Analytics nature=PLATFORM 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": "Algood Men",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Big Data Engineer",
"ETL Developer",
"Data Pipeline Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "Technical Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 BigData skills like Kafka",
"last_5_words": "bottle-necks and resolve the same"
},
"text": "\u2022 BigData skills like Kafka and NiFI, AWS Services like S3\n\u2022 ETL workflow development and building ETL pipelines using NiFi\n\u2022 Experience in the field of Big Data Technologies, software programming and developing\n\u2022 Knowledge of ETL framework with Snowflake cloud technology\n\u2022 Experience with big data tools Spark, Hadoop Kafka would be good to have\n\u2022 Hands on experience in Microsoft Azure Data Factory Azure Logic apps service Data Bricks\n\u2022 Experience integrating different data sources\n\u2022 Participate with team and do requirement analysis and convert into technical documentation\n\u2022 Identify the performance bottle-necks and resolve the same",
"word_count": 104
},
{
"bullet_count": 5,
"heading": "Soft skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Good communication(Written and Verbal)",
"last_5_words": "organization and customer side."
},
"text": "\u2022 Good communication(Written and Verbal) and interpersonal skills\n\u2022 The ability to plan work and meet deadlines\n\u2022 Accuracy and attention to detail\n\u2022 Should be able to work in a team or as an individual contributor\n\u2022 To coordinate with developers / other architects / other stakeholders and cross functional teams from organization and customer side.",
"word_count": 66
},
{
"bullet_count": 0,
"heading": "Key skillset",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Nifi, Data Management, Data",
"last_5_words": "Data Management, Data Analysis"
},
"text": "Nifi, Data Management, Data Analysis",
"word_count": 5
}
],
"urls": [
{
"type": "other",
"url": "mailto:Apply@algoodmen.com"
},
{
"type": "other",
"url": "mailto:Amy@algoodmen.com"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Kafka"
},
{
"is_primary": true,
"skill_name": "Apache NiFi"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Amazon S3"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Snowflake"
},
{
"is_primary": false,
"skill_name": "Apache Spark"
},
{
"is_primary": false,
"skill_name": "Hadoop"
},
{
"is_primary": true,
"skill_name": "Microsoft Azure"
},
{
"is_primary": true,
"skill_name": "Azure Data Factory"
},
{
"is_primary": true,
"skill_name": "Azure Logic Apps"
},
{
"is_primary": true,
"skill_name": "Azure Databricks"
}
],
"jd_role": {
"display_name": "Data Engineer",
"rationale": null,
"role_aliases": [
"Big Data Engineer",
"ETL Developer",
"Data Pipeline Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Algood Men",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years"
},
"job_locations": [],
"role": "Data Engineer",
"role_aliases": [
"Big Data Engineer",
"ETL Developer",
"Data Pipeline Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "Technical Skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 BigData skills like Kafka",
"last_5_words": "bottle-necks and resolve the same"
},
"text": "\u2022 BigData skills like Kafka and NiFI, AWS Services like S3\n\u2022 ETL workflow development and building ETL pipelines using NiFi\n\u2022 Experience in the field of Big Data Technologies, software programming and developing\n\u2022 Knowledge of ETL framework with Snowflake cloud technology\n\u2022 Experience with big data tools Spark, Hadoop Kafka would be good to have\n\u2022 Hands on experience in Microsoft Azure Data Factory Azure Logic apps service Data Bricks\n\u2022 Experience integrating different data sources\n\u2022 Participate with team and do requirement analysis and convert into technical documentation\n\u2022 Identify the performance bottle-necks and resolve the same",
"word_count": 104
},
{
"bullet_count": 5,
"heading": "Soft skills",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Good communication(Written and Verbal)",
"last_5_words": "organization and customer side."
},
"text": "\u2022 Good communication(Written and Verbal) and interpersonal skills\n\u2022 The ability to plan work and meet deadlines\n\u2022 Accuracy and attention to detail\n\u2022 Should be able to work in a team or as an individual contributor\n\u2022 To coordinate with developers / other architects / other stakeholders and cross functional teams from organization and customer side.",
"word_count": 66
},
{
"bullet_count": 0,
"heading": "Key skillset",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Nifi, Data Management, Data",
"last_5_words": "Data Management, Data Analysis"
},
"text": "Nifi, Data Management, Data Analysis",
"word_count": 5
}
],
"urls": [
{
"type": "other",
"url": "mailto:Apply@algoodmen.com"
},
{
"type": "other",
"url": "mailto:Amy@algoodmen.com"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "eea7a704-e4e0-444b-b423-d0187a8162c5",
"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": "Flutter Developer",
"kra_matches": [
{
"kra_text": "integrate external APIs and data sources",
"sentence": "Experience integrating different data sources",
"similarity": 0.6101
},
{
"kra_text": "collaborate with design, product, and backend teams",
"sentence": "To coordinate with developers / other architects / other stakeholders and cross functional teams from organization and customer side.",
"similarity": 0.6042
},
{
"kra_text": "translate product and design requirements",
"sentence": "Participate with team and do requirement analysis and convert into technical documentation",
"similarity": 0.5169
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 74,
"score": 0.577,
"slug": "flutter-developer",
"total_count": null
},
{
"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": "BigData skills like Kafka and NiFI, AWS Services like S3",
"similarity": 0.6063
},
{
"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": "ETL workflow development and building ETL pipelines using NiFi",
"similarity": 0.5007
},
{
"kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "Experience integrating different data sources",
"similarity": 0.4899
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5323,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Experience integrating different data sources",
"similarity": 0.5885
},
{
"kra_text": "performance tuning",
"sentence": "Identify the performance bottle-necks and resolve the same",
"similarity": 0.4966
},
{
"kra_text": "backend data integration",
"sentence": "BigData skills like Kafka and NiFI, AWS Services like S3",
"similarity": 0.4279
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.5043,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "To coordinate with developers / other architects / other stakeholders and cross functional teams from organization and customer side.",
"similarity": 0.571
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Participate with team and do requirement analysis and convert into technical documentation",
"similarity": 0.4969
},
{
"kra_text": "Evaluates cloud-native managed services, serverless compute, PaaS databases, and CDN solutions for workload fit and total cost of ownership.",
"sentence": "BigData skills like Kafka and NiFI, AWS Services like S3",
"similarity": 0.4233
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.497,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "Pega Developer",
"kra_matches": [
{
"kra_text": "Requirements analysis and process translation",
"sentence": "Participate with team and do requirement analysis and convert into technical documentation",
"similarity": 0.591
},
{
"kra_text": "external system integration implementation",
"sentence": "Experience integrating different data sources",
"similarity": 0.4415
},
{
"kra_text": "defect troubleshooting and resolution",
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"similarity": 0.4319
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 24,
"score": 0.4881,
"slug": "pega-developer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 5,
"matched_skills": [
"AWS",
"Amazon S3",
"Kafka",
"Microsoft Azure",
"Snowflake"
],
"role_id": 2,
"score": 0.5,
"slug": "data-engineer",
"total_count": 10
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Kafka",
"Microsoft Azure"
],
"role_id": 1,
"score": 0.3,
"slug": "backend-engineer",
"total_count": 10
},
{
"display_name": "Go Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Kafka",
"Microsoft Azure"
],
"role_id": 81,
"score": 0.3,
"slug": "go-backend-developer",
"total_count": 10
},
{
"display_name": "Python Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Kafka",
"Microsoft Azure"
],
"role_id": 80,
"score": 0.3,
"slug": "python-backend-developer",
"total_count": 10
},
{
"display_name": "Node.js Backend Developer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Kafka",
"Microsoft Azure"
],
"role_id": 82,
"score": 0.3,
"slug": "node-backend-developer",
"total_count": 10
}
]
},
"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 0.50 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 376,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 17845,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Apache NiFi",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 17846,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 17847,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Data Factory",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 17848,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Logic Apps",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 17850,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Azure Databricks",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 173,
"existing_alias_text": "Kafka",
"input_term": "Kafka",
"matched_canonical": {
"category_id": 3,
"display_name": "Kafka",
"id": 36,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "kafka",
"sub_category_id": 3533,
"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": 406,
"existing_alias_text": "AWS",
"input_term": "AWS",
"matched_canonical": {
"category_id": 9,
"display_name": "AWS",
"id": 187,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "aws",
"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": 379,
"existing_alias_text": "Amazon S3",
"input_term": "Amazon S3",
"matched_canonical": {
"category_id": 11,
"display_name": "Amazon S3",
"id": 170,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CLOUD_SERVICE",
"slug": "amazon-s3",
"sub_category_id": 120,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 299,
"existing_alias_text": "Snowflake",
"input_term": "Snowflake",
"matched_canonical": {
"category_id": 9,
"display_name": "Snowflake",
"id": 105,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "snowflake",
"sub_category_id": 113,
"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": 2004,
"existing_alias_text": "Apache Spark",
"input_term": "Apache Spark",
"matched_canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2010,
"existing_alias_text": "Hadoop",
"input_term": "Hadoop",
"matched_canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"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": 258,
"existing_alias_text": "Microsoft Azure",
"input_term": "Microsoft Azure",
"matched_canonical": {
"category_id": 9,
"display_name": "Microsoft Azure",
"id": 97,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "microsoft-azure",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": ".NET Backend Developer",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
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"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
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"slug": "devops-engineer",
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},
{
"display_name": "Fullstack Developer",
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"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
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"role_archetype": "Engineering",
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},
{
"display_name": "ML Engineer",
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"slug": "ml-engineer",
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},
{
"display_name": "MLOps Engineer",
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"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "AI Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Cloud Architect",
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"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"
},
{
"display_name": "Engineering Manager",
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"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"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 0.50 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Asynchronous Messaging and Event Streaming",
"id": 297,
"rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
"slug": "asynchronous-messaging-and-event-streaming",
"source": "db"
},
"input_skill": "Kafka",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
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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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"rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
"slug": "messaging-and-background-jobs",
"source": "db"
},
"input_skill": "Kafka",
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"roles_from_db": [
{
"display_name": "PHP Backend Developer",
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"rationale": null,
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"source": "db"
},
{
"display_name": "Python Backend Developer",
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"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
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"role_archetype": "Engineering",
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}
]
},
{
"dimension": {
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"rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
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{
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{
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]
},
{
"dimension": {
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{
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},
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},
{
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"slug": "data-engineer",
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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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},
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]
},
{
"dimension": {
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"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.",
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"source": "db"
},
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{
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}
]
},
{
"dimension": {
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"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"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": "Snowflake",
"alias_type": "CANONICAL",
"id": 299,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Snowflake",
"id": 105,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "snowflake",
"sub_category_id": 113,
"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": "Snowflake",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Snowflake",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Apache Spark",
"alias_type": "CANONICAL",
"id": 2004,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "apache spark 3",
"alias_type": "VERSION",
"id": 2006,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark",
"alias_type": "VERSION",
"id": 2510,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3",
"alias_type": "VERSION",
"id": 2007,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark 3.x",
"alias_type": "VERSION",
"id": 2009,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "spark3",
"alias_type": "VERSION",
"id": 2008,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Apache Spark",
"id": 1350,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "apache-spark",
"sub_category_id": 1021,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Apache Spark",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Apache Spark",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Hadoop",
"alias_type": "CANONICAL",
"id": 2010,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Hadoop",
"id": 1351,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "hadoop",
"sub_category_id": 91,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"input_skill": "Hadoop",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
}
],
"input_skill": "Hadoop",
"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": "Microsoft Azure",
"alias_type": "CANONICAL",
"id": 258,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 9,
"display_name": "Microsoft Azure",
"id": 97,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "microsoft-azure",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud \u0026 Hosting Providers",
"id": 414,
"rationale": "Knowledge of major cloud and hosting vendor platforms for deploying and managing PHP applications.",
"slug": "cloud-hosting-providers",
"source": "db"
},
"input_skill": "Microsoft Azure",
"llm_role": null,
"roles_from_db": [
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"input_skill": "Microsoft Azure",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Microsoft Azure",
"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": "Azure Data Factory",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"sub_category": "Data Integration",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "azure-data-factory",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Azure Logic Apps",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"sub_category": "Integration Services",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "azure-logic-apps",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Azure Databricks",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "PLATFORM",
"sub_category": "Data Analytics",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "azure-databricks",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Apache NiFi",
"ETL",
"Azure Data Factory",
"Azure Logic Apps",
"Azure Databricks"
]
}
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 0.50 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Kafka",
"tag": "in_db"
},
{
"skill": "Apache NiFi",
"tag": "new"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Amazon S3",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Snowflake",
"tag": "in_db"
},
{
"skill": "Apache Spark",
"tag": "in_db"
},
{
"skill": "Hadoop",
"tag": "in_db"
},
{
"skill": "Microsoft Azure",
"tag": "in_db"
},
{
"skill": "Azure Data Factory",
"tag": "new"
},
{
"skill": "Azure Logic Apps",
"tag": "new"
},
{
"skill": "Azure Databricks",
"tag": "new"
}
],
"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": "Asynchronous Messaging and Event Streaming",
"id": 297,
"rationale": "Asynchronous communication patterns and broker technologies used to decouple backend services and move work off the request path. Includes queues, pub/sub, event streams, consumer groups, dead-letter queues, and delivery semantics across systems such as Kafka, RabbitMQ, NATS, SQS/SNS, Pulsar, and ActiveMQ.",
"slug": "asynchronous-messaging-and-event-streaming",
"source": "db"
},
"dimension_id": 297,
"input_skill": "Kafka",
"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": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-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": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Background Jobs",
"id": 291,
"rationale": "Asynchronous processing patterns and worker systems used to decouple backend work from request handling. This is a coherent cluster because the role supports background jobs, retries, and deferred processing.",
"slug": "messaging-and-background-jobs",
"source": "db"
},
"dimension_id": 291,
"input_skill": "Kafka",
"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": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Event Streaming",
"id": 8,
"rationale": "Transport-layer systems used to move events and decouple producers from consumers. Data engineers use these systems to ingest, buffer, and distribute event data before downstream processing.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"dimension_id": 8,
"input_skill": "Kafka",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 36,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "AWS",
"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": 187,
"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": "AWS",
"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": 187,
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