Pipeline run
4760d063-d89b-45bb-95db-040d09385c1e
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 dotnet-backend-developer 0.11 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Company Description
Accrualify India Private Limited is a subsidiary of Accrualify, Inc., San Mateo USA, and part of a conglomerate gaint Fleetcor Technologies, Inc. Our objective is to provide extended support to the core Information Technology (IT) and Business support teams of Accrualify, Inc by developing and maintaining Corpay-Complete software product.
Role Description
Accrualify India Private Limited is looking for a Senior Data & BI Engineer to join our team in Nagpur. This is a full-time, on-site role, responsible for designing and implementing data solutions that support Accrualify, Inc. You will work closely with other engineers and stakeholders to ensure data solutions are scalable and optimized to meet business requirements.
Senior Data & BI Engineer ( 4-8 yrs exp )
Role Responsibilities
● Capture technical specifications, design, develop, test, implement, and support optimal data solutions including data warehouses, data stores, data lakes, dashboards and reports.
● Examine sources of data through user interviews and data object analysis.
● Craft optimal strategies for data integration process.
● Responsible for development and deployment of new data platforms
● Develop queries, views and stored procedures for ETL processes and pipelines.
● Document data mappings, data dictionaries/definitions, processes, programs and solutions as per established standards for data governance.
● Create, execute, and document unit test plans for data processes and programs.
● Responsible for creating reusable and scalable data pipelines
● Design and develop API solutions using data integration tools for interfacing between ERP source applications and Enterprise Data sources.
● Oversee the Enterprise Data Management (EDM) governance process to ensure “a streamlined, standardized system for the company to find, manage, access, store, and secure their data”.
● Document and organize results and solutions for leadership and stakeholders, and address questions.
● Implement data integration through the development of shared databases, modification and optimization of current systems, managing the exchange and storing of data, and making recommendations when upgrades or changes are needed to maintain smooth operations.
● Responsible for creating reusable components for rapid development of data platform
● Make improvements to the Data warehouse processes by crafting and assessing new formats for data interchange, rewriting expectations and procedures as needed, and improving logical and physical design.
● Optimize data lake performance by dealing with any data conflicts that arise and keeping data definitions up to date.
● Share & implement best practices by keeping track of new technologies and strategies for DW/data API/ETL, partaking in workshops and other educational opportunities, widening personal networks and reading industry publications.
Qualifications & Skills
● Bachelor’s degree in mathematics, statistics, computer science, data science, information technology, engineering or related field
● 4+ years of working experience in ETL, data warehousing, data lakes, reporting and operational data store.
● Expertise in Microsoft SQL Server database and T-SQL is a must
● Experience in creating complex stored procedures, materialized views
● Experience in creating data pipelines using Azure Data Factory and T-SQL
● Hands-on experience in implementing Azure cloud Data warehouse with hybrid data ingestion scenario’s
● Good understanding of OLAP data models design
● Must have strong knowledge in Data warehouse design, modelling, ETL.
● Must have strong knowledge in Azure cloud technologies and services
● Hands on Experience in implementing Azure DevOps/ Git Hub for code versioning and deployments
● Knowledge of Power shell scripting
● Hands-on experience in Enterprise architecture to understand/change the downstream applications as needed.
● Microsoft Power BI, No SQL, Synapse, Databricks experience a plus.
● Experience in other cloud platforms like AWS a plus
● Exposure with Big Data design, development and related technologies: AWS /Redshift, Hadoop, HDFS, along with reporting/analytical tools like Tableau and Alteryx.
● Proven experience in creating, maintaining and collectively facilitating an Enterprise Data Management (EDM) governance process with the business data stewards.
● Python, Java, and Scala programming languages a plus.
● Must possess strong communication, collaboration, and presentation soft skills.
● Strong analytical problem-solving skills
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- 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
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
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) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- dashboards (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Dashboarding
- Confidence
- 0.80
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Dashboarding is a common requirement in BI/observability JDs and is supported by major vendors like Grafana, Power BI, and Tableau, indicating broad market adoption rather than a niche toolset.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3485
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Backend Observability, Logging, and Diagnostics Catalog dimension db id 388
Library dimension (catalog)
Roles linked in library: Kotlin Backend Developer, Scala Backend Developer
-
Observability and Incident Response Catalog dimension db id 10
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- SQL (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Query Language
- Vendor
- ANSI
- License
- unknown
- Year introduced
- 1974
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 97
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Views (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Query Builder Framework
- Vendor
- null
- License
- unknown
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: “Views” as a query-builder framework has low JD volume and is largely overshadowed by ORM/query tools like Django ORM, SQLAlchemy, and Knex in current postings and docs.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 2424
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Views and Content Querying Catalog dimension db id 347
Library dimension (catalog)
Roles linked in library: Drupal Dev
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Views and Content Querying
views-and-content-querying
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- LANGUAGE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Unit Testing (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Testing Methodology
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Unit testing is a standard hiring requirement across software JDs and appears in mainstream frameworks/docs; GitHub and Stack Overflow usage remain consistently high, with no successor replacing it.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 44
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- API (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Application Programming Interface
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: APIs are a core requirement in most software engineering JDs and underpin common integrations across cloud, mobile, and web stacks; major vendors like AWS, Stripe, and Google Cloud center products on API-first usage.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1174
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
API Integration and Data Fetching Catalog dimension db id 127
Library dimension (catalog)
Roles linked in library: Angular Frontend Developer, Frontend Developer, Fullstack Developer, React Frontend Developer, Svelte Frontend Developer, Vue Frontend Developer, Web Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- data mapping (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Data Mapping
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data mapping appears broadly in ETL/ELT, integration, and MDM job descriptions across BI and cloud data stacks; vendors like Informatica, dbt, and Azure Data Factory all center it as a core capability.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3239
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
External System Integrations Catalog dimension db id 14
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Drupal Dev, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
External System Integrations
external-system-integrations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Data Lakes (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Data Lake Architecture
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Data lakes are widely listed in cloud/data platform job descriptions and are a standard architecture in AWS, Azure, and GCP ecosystems; they’re a common hiring-pipeline staple rather than a niche pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1025
- 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
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
|
React Frontend Development
d_init_01
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
|---|---|---|---|---|---|---|
| Data Lakes | in_db |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Lakes | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Dashboards | in_db |
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Dashboards | in_db |
Observability and Incident Response
observability-and-incident-response
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| SQL | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Views | in_db |
Views and Content Querying
views-and-content-querying
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Unit Testing | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| API | in_db |
API Integration and Data Fetching
api-integration-and-data-fetching
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Mapping | in_db |
External System Integrations
external-system-integrations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Data Lake | new |
Cloud Storage and Data Services
cloud-storage-and-data-services
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Data Lake | new |
React Frontend Development
d_init_01
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Warehousing | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Reports | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Stored Procedures | type=Databases subtype=general nature=LANGUAGE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Integration | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Governance | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Dictionary | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Enterprise Data Management | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehouse | type=Databases subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Interchange | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Logical Design | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Physical Design | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Data Lake ↔ Cloud Storage and Data Services | |
| dimension_skill_link_proposed | Data Lake ↔ React Frontend Development |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Accrualify India Private Limited is",
"last_5_words": "maintaining Corpay-Complete software product."
},
"text": "Accrualify India Private Limited is a subsidiary of Accrualify, Inc., San Mateo USA, and part of a conglomerate gaint Fleetcor Technologies, Inc. Our objective is to provide extended support to the core Information Technology (IT) and Business support teams of Accrualify, Inc by developing and maintaining Corpay-Complete software product.",
"word_count": 47
},
"certifications": [],
"company_name": "Accrualify India Private Limited",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"ITES",
"BPO",
"Tech Consulting"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Mathematics / Statistics / Computer Science / Data Science / Information Technology / Engineering (or related)",
"raw": "Bachelor\u2019s degree in mathematics, statistics, computer science, data science, information technology, engineering or related field",
"requirement": "required"
}
],
"experience": {
"max": 8,
"min": 4,
"raw": "4-8 yrs exp"
},
"job_locations": [
{
"aliases": [
"Nagpur, MH"
],
"city": "Nagpur",
"country": "India",
"state": null,
"work_mode": "onsite"
}
],
"role": "Senior Data \u0026 BI Engineer",
"role_aliases": [
"Data Engineer",
"BI Engineer",
"Senior Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 15,
"heading": "Role Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u25cf Capture technical specifications, design,",
"last_5_words": "and reading industry publications."
},
"text": "\u25cf Capture technical specifications, design, develop, test, implement, and support optimal data solutions including data warehouses, data stores, data lakes, dashboards and reports.\n\u25cf Examine sources of data through user interviews and data object analysis.\n\u25cf Craft optimal strategies for data integration process.\n\u25cf Responsible for development and deployment of new data platforms.\n\u25cf Develop queries, views and stored procedures for ETL processes and pipelines.\n\u25cf Document data mappings, data dictionaries/definitions, processes, programs and solutions as per established standards for data governance.\n\u25cf Create, execute, and document unit test plans for data processes and programs.\n\u25cf Responsible for creating reusable and scalable data pipelines.\n\u25cf Design and develop API solutions using data integration tools for interfacing between ERP source applications and Enterprise Data sources.\n\u25cf Oversee the Enterprise Data Management (EDM) governance process to ensure \u201ca streamlined, standardized system for the company to find, manage, access, store, and secure their data\u201d.\n\u25cf Document and organize results and solutions for leadership and stakeholders, and address questions.\n\u25cf Implement data integration through the development of shared databases, modification and optimization of current systems, managing the exchange and storing of data, and making recommendations when upgrades or changes are needed to maintain smooth operations.\n\u25cf Responsible for creating reusable components for rapid development of data platform.\n\u25cf Make improvements to the Data warehouse processes by crafting and assessing new formats for data interchange, rewriting expectations and procedures as needed, and improving logical and physical design.\n\u25cf Optimize data lake performance by dealing with any data conflicts that arise and keeping data definitions up to date.\n\u25cf Share \u0026 implement best practices by keeping track of new technologies and strategies for DW/data API/ETL, partaking in workshops and other educational opportunities, widening personal networks and reading industry publications.",
"word_count": 309
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Data Warehousing"
},
{
"is_primary": true,
"skill_name": "Data Lakes"
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API 2 — extract-details
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"volatility": "STABLE"
},
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{
"dimension": {
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"id": 347,
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"slug": "views-and-content-querying",
"source": "db"
},
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{
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}
]
}
],
"input_skill": "Views",
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},
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},
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},
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},
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{
"alias_text": "Unit Testing",
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],
"canonical": {
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{
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},
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}
],
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},
{
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{
"alias_text": "API",
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}
],
"canonical": {
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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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},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Governance",
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"new_alias_persisted": false,
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},
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},
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},
{
"aliases_in_db": [
{
"alias_text": "data mapping",
"alias_type": "CANONICAL",
"id": 3824,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
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"typical_lifespan": "EVERGREEN",
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},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "External System Integrations",
"id": 14,
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"slug": "external-system-integrations",
"source": "db"
},
"input_skill": "Data Mapping",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
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},
{
"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": "Drupal Dev",
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},
{
"display_name": "Java Backend Developer",
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},
{
"display_name": "Kotlin Backend Developer",
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"slug": "kotlin-server-backend-developer",
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},
{
"display_name": "Node.js Backend Developer",
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},
{
"display_name": "PHP Backend Developer",
"id": 86,
"rationale": null,
"role_archetype": "Engineering",
"slug": "php-backend-developer",
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},
{
"display_name": "Python Backend Developer",
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},
{
"display_name": "Ruby Backend Developer",
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},
{
"display_name": "Scala Backend Developer",
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"slug": "scala-backend-developer",
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}
]
}
],
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},
{
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},
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},
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},
{
"aliases_in_db": [],
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"input_skill": "Data Pipelines",
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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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"input_skill": "Data Warehouse",
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},
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},
{
"aliases_in_db": [
{
"alias_text": "Data Lakes",
"alias_type": "CANONICAL",
"id": 2017,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 1,
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"id": 1358,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PATTERN",
"slug": "data-lakes",
"sub_category_id": 1025,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"slug": "cloud-storage-and-data-services",
"source": "db"
},
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{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
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}
]
},
{
"dimension": {
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"source": "db"
},
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"roles_from_db": []
}
],
"input_skill": "Data Lake",
"matched_via": "embedding_alias",
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
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"dimensions": [],
"input_skill": "Data Interchange",
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"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",
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"volatility": "MEDIUM"
},
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},
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},
{
"aliases_in_db": [],
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"dimensions": [],
"input_skill": "Logical Design",
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"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
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"category": "Data Engineering Tools",
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},
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"skill_id": "logical-design",
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},
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"was_in_llm_skills": true
},
{
"aliases_in_db": [],
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"dimensions": [],
"input_skill": "Physical Design",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
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},
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},
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}
],
"unmatched_skills": [
"Data Warehousing",
"Reports",
"ETL",
"Stored Procedures",
"Data Integration",
"Data Governance",
"Data Dictionary",
"Data Pipelines",
"Enterprise Data Management",
"Data Warehouse",
"Data Interchange",
"Logical Design",
"Physical Design"
]
}
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 dotnet-backend-developer 0.11 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": "Data Lakes",
"tag": "in_db"
},
{
"skill": "Dashboards",
"tag": "in_db"
},
{
"skill": "Reports",
"tag": "new"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "Views",
"tag": "in_db"
},
{
"skill": "Stored Procedures",
"tag": "new"
},
{
"skill": "Unit Testing",
"tag": "in_db"
},
{
"skill": "API",
"tag": "in_db"
},
{
"skill": "Data Integration",
"tag": "new"
},
{
"skill": "Data Governance",
"tag": "new"
},
{
"skill": "Data Mapping",
"tag": "in_db"
},
{
"skill": "Data Dictionary",
"tag": "new"
},
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Enterprise Data Management",
"tag": "new"
},
{
"skill": "Data Warehouse",
"tag": "new"
},
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "Data Interchange",
"tag": "new"
},
{
"skill": "Logical Design",
"tag": "new"
},
{
"skill": "Physical Design",
"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 Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "Data Lakes",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1358,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Data Lakes",
"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": [],
"skill_dimension_saved": true,
"skill_id": 1358,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Backend Observability, Logging, and Diagnostics",
"id": 388,
"rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
"slug": "backend-observability-logging-and-diagnostics",
"source": "db"
},
"dimension_id": 388,
"input_skill": "Dashboards",
"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": "Kotlin Backend Developer",
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{
"display_name": "Scala Backend Developer",
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{
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{
"display_name": ".NET Backend Developer",
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"source": "db"
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{
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"slug": "backend-engineer",
"source": "db"
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{
"display_name": "Node.js Backend Developer",
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"rationale": null,
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{
"display_name": "PHP Backend Developer",
"id": 86,
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"role_archetype": "Engineering",
"slug": "php-backend-developer",
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],
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{
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"display_name": "Pega Programming Languages \u0026 DSLs",
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"matched_chosen_role": false,
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"roles_from_db": [
{
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"slug": "pega-developer",
"source": "db"
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],
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"skill_id": 101,
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{
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"source": "db"
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"dimension_id": 21,
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
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"role_archetype": null,
"slug": "data-engineer",
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"skill_id": 101,
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{
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"display_name": "Views and Content Querying",
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"source": "db"
},
"dimension_id": 347,
"input_skill": "Views",
"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": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
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"source": "db"
}
],
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"skill_id": 3116,
"skill_tag": "in_db",
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{
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"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
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"matched_chosen_role": false,
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"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 517,
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},
{
"chosen_role_id": 2,
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"display_name": "API Integration and Data Fetching",
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"rationale": "Client-side integration with backend endpoints and third-party services, including request shaping, response handling, and synchronization with UI state. This is central to frontend work because most screens depend on remote data.",
"slug": "api-integration-and-data-fetching",
"source": "db"
},
"dimension_id": 127,
"input_skill": "API",
"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": "Angular Frontend Developer",
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"role_archetype": "Engineering",
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"source": "db"
},
{
"display_name": "Frontend Developer",
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"role_archetype": null,
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{
"display_name": "Fullstack Developer",
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{
"display_name": "React Frontend Developer",
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{
"display_name": "Svelte Frontend Developer",
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},
{
"display_name": "Vue Frontend Developer",
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{
"display_name": "Web Developer",
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"role_archetype": null,
"slug": "web-developer",
"source": "db"
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],
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{
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"source": "db"
},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": ".NET Backend Developer",
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{
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{
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"rationale": null,
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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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{
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{
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{
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],
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{
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},
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{
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}
],
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{
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},
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"roles_from_db": [],
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],
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}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.