Pipeline run
3c43e571-a90b-4db2-989d-f48eeee00757
Pipeline LLM cost (USD)
API 1: $0.0032
API 2: $0.0003
API 3: $0.0000
Total: $0.0035
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded
sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work
· Data platform optimization and governance
Build and support AWS cloud data warehousing for insurance/financial clients, including Glue/ETL loads, data transformations, quality gap analysis, and governance/documentation of business definitions and data assets.
""Develop data strategy, data governance process and maintenance of business definitions, category and glossary for data assets""
Tech stack maturity
Modern Cloud Native
AWS is a cloud-first platform commonly used in modern data engineering stacks, indicating a modern cloud-native tech environment.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
Transformers
Evidence — skills matched in JD (7)
Data Warehousing
AWS
AWS Glue
ETL
Cloud Data Warehouse
Data Governance
Business Intelligence
Skill cluster (2 dimension groups, role-scoped)
Cloud Platforms
AWS
Cross-cutting / unaligned
Data Warehousing
AWS Glue
ETL
Cloud Data Warehouse
Data Governance
Business Intelligence
Show KRA description ↓
• Accountable for delivery of data deliveries for top insurance and financial services clients world-wide
• Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms
• Work closely with BI technology vendors for any need arising due to production support, product upgrades, or development.
• Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations
• Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders
• Development AWS Glue jobs and AWS stack for loading structured and unstructured data in Cloud
• Development of data strategy, data governance process and maintenance of business definitions, category and glossary for data assets for top insurance and financial services clients world-wide
• Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse
Signals
Skill
data-engineer
0.17
Alias
data-engineer
1.00
KRA
data-engineer
0.58
Post-classification
Centroidupdated · n=292
Alias collision log—
New-role queue—
New skills captured6
New KRA captured—
Captured for admin review
Data Warehousing
primary
↔
Data Engineer
pending
Business Intelligence
↔
Data Engineer
pending
AWS Glue
primary
↔
Data Engineer
pending
ETL
primary
↔
Data Engineer
pending
Cloud Data Warehouse
primary
↔
Data Engineer
pending
Data Governance
primary
↔
Data Engineer
pending
Status:
completed
Created: 2026-05-27T15:23:33.616158Z
Updated: 2026-06-12T16:32:30.593297Z
API 3 duration: 12734 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
Data Engineer
CASE 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 0.17 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped
Job description
Company Description We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable. Job Description Position Summary: If you’re looking for a career that transforms, inspires, challenges, and rewards you, then come join us! Verisk Analytics is a global supplier of risk assessment services and decision analytics for customers in a variety of markets, including insurance, healthcare, financial services, supply chain, and others. We’re a thriving public company with solid revenue growth and earnings and offices worldwide. And we’re continually looking for ways to augment our existing markets and expand into new markets with excellent growth potential. At Verisk, you’ll be part of an organization that’s committed to serving the long-term interests of our stakeholders, including the communities where we operate. Are you passionate about data engineering in Cloud technologies in most effective way and does creating innovative data management and reporting solutions excite you? Are you looking for an opportunity where you can both practice your technical trade while adding to your leadership, consulting and delivery experience to turn a career corner? If you've got technical chops, consulting skills and are a passionate data architect, please read on... Responsibilities • Accountable for delivery of data deliveries for top insurance and financial services clients world-wide • Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms • Work closely with BI technology vendors for any need arising due to production support, product upgrades, or development. • Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations • Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders • Development AWS Glue jobs and AWS stack for loading structured and unstructured data in Cloud • Development of data strategy, data governance process and maintenance of business definitions, category and glossary for data assets for top insurance and financial services clients world-wide • Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse Qualifications • 3+ years of experience in data modeling and data architecture discipline • Hands on experience in AWS cloud technologies and managing data in AWS • 2+ years of AWS data migration project with AWS Glue, CI/CD technologies • Data Mapping expertise, ability to understand business requirements and derive data dictionary and data assets requirement for ETL and DBA groups • Strong SQL is a must and prior experience in any RDMS experience between SQL Server/Oracle/PostgreSQL is a must • Knowledge and/or experience in the P&C industry is highly desired – along with at least one functional area relevant to these clients – new business, agency, risk management, actuarial, claims and loss • 2+ years of experience in Python programming, specifically in complex data loading using Python • Strong project management skills and attention to detail • Excellent communication skills, both written and verbal • Ability to collaborate with peers and develop good working relationships • Knowledge of data warehousing on the AWS platform is desirable • AWS Certification especially Cloud Practitioner completion is required • Expertise and experience in any ETL Tool especially SSIS/IBM DataStage/Informatica is required. Previous working experience in Talend ETL is preferred and knowledge of Talend Administration Center (TAC) is most desirable. Additional Information In 2022, Verisk received Great Place to Work® Certification for our outstanding workplace culture for the sixth year in a row and second-time certification in the UK, Spain, and India. We’re also one of the 38 companies on the UK’s Best Workplaces™ list and one of 18 companies on Spain’s Best Workplaces™ list. For over fifty years and through innovation, interpretation, and professional insight, Verisk has replaced uncertainty with precision to unlock opportunities that deliver significant and demonstrable impact. From our historic roots in risk assessment, we’ve grown to provide analytic insights that help transform industries focused on some of the world’s most critical areas. Today, the insurance industry relies on Verisk to be, and to make the world, more productive, resilient, and sustainable. Verisk works in collaboration with our customers and at the intersection of people, data, and advanced technologies. Through proprietary platformed analytics, advanced modeling, and interpretation, we deliver immediate and sustained value to our customers and through them, to the individuals and societies they serve, with greater speed, precision, and scale. We’re 9,000 people strong, committed to translating big data into big ideas. We help others see new possibilities and empower certainty into big decisions that impact individuals and societies. And we relentlessly and ethically pursue innovation to help move our customers, and the world, toward better tomorrows. Everyone at Verisk—from our chief executive officer to our newest employee—is guided by The Verisk Way, to Be Remarkable, Add Value, and Innovate. • Be Remarkable by doing something better each day in service to our customers and each other • Add Value by delivering immediate and sustained results that drive positive outcomes • Innovate by redefining what’s possible, embracing challenges, and pushing boundaries Verisk Businesses Underwriting Solutions — provides underwriting and rating solutions for auto and property, general liability, and excess and surplus to assess and price risk with speed and precision Claims Solutions — supports end-to-end claims handling with analytic and automation tools that streamline workflow, improve claims management, and support better customer experiences Property Estimating Solutions — offers property estimation software and tools for professionals in estimating all phases of building and repair to make day-to-day workflows the most efficient Extreme Event Solutions — provides risk modeling solutions to help individuals, businesses, and society become more resilient to extreme events. Specialty Business Solutions — provides an integrated suite of software for full end-to-end management of insurance and reinsurance business, helping companies manage their businesses through efficiency, flexibility, and data governance Marketing Solutions — delivers data and insights to improve the reach, timing, relevance, and compliance of every consumer engagement Life Insurance Solutions – offers end-to-end, data insight-driven core capabilities for carriers, distribution, and direct customers across the entire policy lifecycle of life and annuities for both individual and group. Verisk Maplecroft — provides intelligence on sustainability, resilience, and ESG, helping people, business, and societies become stronger Verisk Analytics is an equal opportunity employer. All members of the Verisk Analytics family of companies are equal opportunity employers. We consider all qualified applicants for employment without regard to race, religion, color, national origin, citizenship, sex, gender identity and/or expression, sexual orientation, veteran's status, age or disability. http://www.verisk.com/careers.html Unsolicited resumes sent to Verisk, including unsolicited resumes sent to a Verisk business mailing address, fax machine or email address, or directly to Verisk employees, will be considered Verisk property. Verisk will NOT pay a fee for any placement resulting from the receipt of an unsolicited resume.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Data Warehousing
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
AWS
Primary
Library skill
API 3: existing canonical (in_db)
Existing skill (matched library)
Canonical: AWS
id=187
· aws
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
API Gateway
AWS CLI
Auto Scaling
CloudFormation
CloudFront
CloudTrail
CloudWatch
Cognito
DynamoDB
EC2
ECS
EKS
Elastic Beanstalk
Elastic Load Balancing
IAM
KMS
Lambda
RDS
Route 53
S3
SNS
SQS
Serverless
VPC
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) |
Business Intelligence
Secondary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
AWS Glue
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Cloud Platforms
- Sub-category
- Data Engineering Tools
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
ETL
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Cloud Data Warehouse
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Data Governance
Primary
New / orchestrated
API 3: new canonical path (new)
New / unmatched skill (orchestrated in API 2)
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
Derived legacy fields
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
|---|---|---|---|---|---|---|
| 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) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Business Intelligence | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | AWS Glue | type=Cloud Platforms subtype=Data Engineering Tools nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Cloud Data Warehouse | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Architect
CompanyVerisk Analytics
Experience3+ years of experience in data modeling and data architecture discipline
DomainIT Services & Consulting
JD type
pass
Certifications
AWS Certification
AWS Cloud Practitioner
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "We help the world see",
"last_5_words": "stronger, more resilient, and sustainable."
},
"text": "We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable.",
"word_count": 36
},
"certifications": [
"AWS Certification",
"AWS Cloud Practitioner"
],
"company_name": "Verisk Analytics",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Analytics",
"Risk Assessment"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years of experience in data modeling and data architecture discipline"
},
"job_locations": [],
"role": "Data Architect",
"role_aliases": [
"Data Engineer",
"Cloud Data Architect",
"Data Engineer/Architect"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Accountable for delivery of",
"last_5_words": "data transformation requirements in to"
},
"text": "\u2022 Accountable for delivery of data deliveries for top insurance and financial services clients world-wide \n\u2022 Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms \n\u2022 Work closely with BI technology vendors for any need arising due to production support, product upgrades, or development. \n\u2022 Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations \n\u2022 Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders \n\u2022 Development AWS Glue jobs and AWS stack for loading structured and unstructured data in Cloud \n\u2022 Development of data strategy, data governance process and maintenance of business definitions, category and glossary for data assets for top insurance and financial services clients world-wide \n\u2022 Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse",
"word_count": 164
}
],
"urls": [
{
"type": "careers",
"url": "http://www.verisk.com/careers.html"
}
]
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": false,
"skill_name": "Business Intelligence"
},
{
"is_primary": true,
"skill_name": "AWS Glue"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Cloud Data Warehouse"
},
{
"is_primary": true,
"skill_name": "Data Governance"
}
],
"jd_role": {
"display_name": "Data Architect",
"rationale": null,
"role_aliases": [
"Data Engineer",
"Cloud Data Architect",
"Data Engineer/Architect"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "We help the world see",
"last_5_words": "stronger, more resilient, and sustainable."
},
"text": "We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable.",
"word_count": 36
},
"certifications": [
"AWS Certification",
"AWS Cloud Practitioner"
],
"company_name": "Verisk Analytics",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Analytics",
"Risk Assessment"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 3,
"raw": "3+ years of experience in data modeling and data architecture discipline"
},
"job_locations": [],
"role": "Data Architect",
"role_aliases": [
"Data Engineer",
"Cloud Data Architect",
"Data Engineer/Architect"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 8,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Accountable for delivery of",
"last_5_words": "data transformation requirements in to"
},
"text": "\u2022 Accountable for delivery of data deliveries for top insurance and financial services clients world-wide \n\u2022 Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms \n\u2022 Work closely with BI technology vendors for any need arising due to production support, product upgrades, or development. \n\u2022 Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations \n\u2022 Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders \n\u2022 Development AWS Glue jobs and AWS stack for loading structured and unstructured data in Cloud \n\u2022 Development of data strategy, data governance process and maintenance of business definitions, category and glossary for data assets for top insurance and financial services clients world-wide \n\u2022 Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse",
"word_count": 164
}
],
"urls": [
{
"type": "careers",
"url": "http://www.verisk.com/careers.html"
}
]
},
"rejected": false,
"rejection_reason": null,
"run_id": "3c43e571-a90b-4db2-989d-f48eeee00757",
"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": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms",
"similarity": 0.5993
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse",
"similarity": 0.5852
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations",
"similarity": 0.5513
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5786,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "backend data integration",
"sentence": "Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations",
"similarity": 0.499
},
{
"kra_text": "backend data integration",
"sentence": "Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms",
"similarity": 0.4811
},
{
"kra_text": "backend data integration",
"sentence": "Work closely with ETL team for implementing data transformation requirements in to the cloud data warehouse",
"similarity": 0.4594
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.4799,
"slug": "svelte-frontend-developer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
"sentence": "Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms",
"similarity": 0.4677
},
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
"sentence": "Development AWS Glue jobs and AWS stack for loading structured and unstructured data in Cloud",
"similarity": 0.467
},
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
"sentence": "Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations",
"similarity": 0.457
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.4639,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "Cloud Security Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements cloud security controls including KMS encryption, secrets management, and data-at-rest protection for AWS, Azure, or GCP workloads.",
"sentence": "Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms",
"similarity": 0.4862
},
{
"kra_text": "Assesses security risk and compliance posture of new cloud services, third-party SaaS integrations, and infrastructure architecture changes.",
"sentence": "Document data sources on client project and perform gap analysis and identify inflow of bad data into Cloud data warehouse and develop recommendations",
"similarity": 0.4584
},
{
"kra_text": "Documents cloud security standards, approved architecture patterns, security exceptions, and remediation guidance for engineering teams.",
"sentence": "Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders",
"similarity": 0.4462
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 23,
"score": 0.4636,
"slug": "cloud-security-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Development of database solutions by designing proposed system; defining database physical structure and functional capabilities, security, back-up, and recovery specifications to internal and external stakeholders",
"similarity": 0.4984
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Work closely with BI technology vendors for any need arising due to production support, product upgrades, or development.",
"similarity": 0.4412
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Implement data warehousing solutions in AWS cloud, analytics data stores in advanced analytics platforms",
"similarity": 0.4194
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.453,
"slug": "full-stack-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"AWS"
],
"role_id": 2,
"score": 0.1667,
"slug": "data-engineer",
"total_count": 6
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"AWS"
],
"role_id": 3,
"score": 0.1667,
"slug": "ml-engineer",
"total_count": 6
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"AWS"
],
"role_id": 5,
"score": 0.1667,
"slug": "cybersecurity-engineer",
"total_count": 6
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"AWS"
],
"role_id": 9,
"score": 0.1667,
"slug": "cloud-architect",
"total_count": 6
},
{
"display_name": "Backend Developer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"AWS"
],
"role_id": 1,
"score": 0.1667,
"slug": "backend-engineer",
"total_count": 6
}
]
},
"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 0.17 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 292,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 14070,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Warehousing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 14071,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Business Intelligence",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 14072,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "AWS Glue",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 14073,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "ETL",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 14074,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Cloud Data Warehouse",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 14075,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Governance",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 406,
"existing_alias_text": "AWS",
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"matched_canonical": {
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "aws",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
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},
{
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"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
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"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
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"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",
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"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
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"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
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"role_archetype": null,
"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": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
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},
{
"display_name": "Scala Backend Developer",
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"role_archetype": "Engineering",
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},
{
"display_name": "AI Engineer",
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"slug": "ai-engineer",
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},
{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
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"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Engineering Manager",
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"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
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}
],
"chosen_role": {
"display_name": "Data Engineer",
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"rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.17 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
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},
"dimensions": [
{
"dimension": {
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},
"input_skill": "AWS",
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"roles_from_db": [
{
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},
{
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},
{
"display_name": "Cyber Security Engineer",
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},
{
"display_name": "Data Engineer",
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},
{
"display_name": "DevOps Engineer",
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},
{
"display_name": "Fullstack Developer",
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},
{
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},
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},
{
"display_name": "Kotlin Backend Developer",
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},
{
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},
{
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},
{
"display_name": "Node.js Backend Developer",
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},
{
"display_name": "Python Backend Developer",
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},
{
"display_name": "Scala Backend Developer",
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]
},
{
"dimension": {
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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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{
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{
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]
},
{
"dimension": {
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"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
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},
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]
}
],
"input_final_skills": [
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],
"input_llm_skills": [
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"new_aliases_persisted": 0,
"run_id": "3c43e571-a90b-4db2-989d-f48eeee00757",
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{
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"new_skill_meta": {
"derived": {
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},
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"typed": null,
"warnings": []
},
"source_tag": "llm",
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},
{
"aliases_in_db": [
{
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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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"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "AWS",
"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": "Business Intelligence",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "business-intelligence",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "AWS Glue",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Cloud Platforms",
"skill_nature": "TOOL",
"sub_category": "Data Engineering Tools",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "aws-glue",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "ETL",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "etl",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Cloud Data Warehouse",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "cloud-data-warehouse",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Governance",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-governance",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Data Warehousing",
"Business Intelligence",
"AWS Glue",
"ETL",
"Cloud Data Warehouse",
"Data Governance"
]
}
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 0.17 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Data Warehousing",
"tag": "new"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Business Intelligence",
"tag": "new"
},
{
"skill": "AWS Glue",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Cloud Data Warehouse",
"tag": "new"
},
{
"skill": "Data Governance",
"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": "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,
"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": "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": "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": 187,
"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": "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": "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": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"dimension_id": 477,
"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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"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": "3c43e571-a90b-4db2-989d-f48eeee00757"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.
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