Pipeline run
c9b2985f-96f8-4315-8d64-ebc774498e2d
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Engineer
CASE Aslug: data-engineer · id: 2 · source: db
Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.20 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Role: Databricks PySpark Developer Experience: 5+ years Location: Bangalore (onsite-5days) /no relocation candidates Notice period-immediate joiners/serving notice period Role Overview : We are looking for a highly skilled Databricks PySpark Developer to join our data platform implementation team. In this role, you will be responsible for designing, developing, and optimizing scalable ETL pipelines and data workflows using Databricks and Apache Spark. You will work closely with data engineers, data scientists, and BI teams to support advanced analytics and reporting requirements. Key Responsibilities : • ETL Development & Data Engineering Design, develop, and maintain scalable ETL processes using Databricks PySpark. Extract, transform, and load data from heterogeneous sources into Data Lake and Data Warehouse environments. Optimize ETL workflows for performance, scalability, and cost efficiency using Spark SQL and PySpark. Implement robust error handling, logging, and monitoring mechanisms for ETL jobs. Design and implement data solutions following Medallion Architecture (Bronze, Silver, Gold layers). Ensure data is cleansed, enriched, validated, and optimized at each layer for analytics consumption. • Data Pipeline Management Hands-on experience in building and managing advanced data pipelines using Databricks Workflows. Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity. Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines. • Data Analysis & Query Optimization Write, review, and optimize complex SQL queries for data transformation, aggregation, and analysis. Perform query tuning and performance optimization on large-scale datasets within Databricks. • Project Coordination & Continuous Improvement Participate in project planning, estimation, and delivery activities. Stay updated with the latest features in Databricks, Spark, and cloud data platforms, and recommend best practices. Document ETL processes, data lineage, metadata, and workflows to support data governance and compliance. Mentor junior developers and contribute to team knowledge sharing where required. Required Qualifications : Bachelor’s degree in Computer Science, Engineering, or a related field. 5+ years of experience in ETL/Data Engineering roles with strong focus on Databricks PySpark. Strong proficiency in Python, with hands-on experience in developing and debugging PySpark applications. In-depth understanding of Apache Spark architecture, including RDDs, DataFrames, and Spark SQL. Expertise in SQL development and optimization for large-scale data processing. Proven experience working with data warehousing concepts and ETL frameworks. Strong problem-solving and troubleshooting skills. Excellent communication and collaboration skills. Preferred Qualifications : Experience working on cloud platforms, preferably AWS. Hands-on experience with tools such as Databricks, Snowflake, Tableau, or similar data platforms. Strong understanding of data governance, data quality, and best practices in data engineering. Relevant certifications in Databricks, PySpark, Spark SQL, or cloud technologies.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Databricks (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Data Analytics Platform
- Vendor
- Databricks, Inc.
- License
- other_open
- Year introduced
- 2013
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 911
- 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
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Aliases — catalog
- Apache Spark (CANONICAL)
- apache spark 3 (VERSION)
- spark (VERSION)
- spark 3 (VERSION)
- spark 3.x (VERSION)
- spark3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Distributed Data Processing Framework
- Vendor
- Apache Software Foundation
- License
- apache_2
- Year introduced
- 2010
- Confidence
- 0.94
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 1021
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ETL and ELT Tooling Catalog dimension db id 24
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- 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 |
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
- 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
- Databases
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- 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
- TOOL
- 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 |
|---|---|---|---|---|---|---|
| Databricks | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PySpark | new |
ETL and ELT Tooling
etl-and-elt-tooling
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
| Apache Spark | in_db |
ETL and ELT Tooling
etl-and-elt-tooling
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| 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 | |
| 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 | Spark SQL | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Warehouse | type=Databases subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Medallion Architecture | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Databricks Workflows | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | PySpark ↔ ETL and ELT Tooling | |
| role_dimension_link_proposed | Data Engineer ↔ ETL and ELT Tooling | |
| 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
Certifications
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [
"Databricks",
"PySpark",
"Spark SQL"
],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science (or related)",
"raw": "Bachelor\u2019s degree in Computer Science, Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years"
},
"job_locations": [
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": null,
"work_mode": "onsite"
}
],
"role": "Databricks PySpark Developer",
"role_aliases": [
"PySpark Developer",
"ETL Developer",
"Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Role Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are looking for a",
"last_5_words": "analytics and reporting requirements."
},
"text": "We are looking for a highly skilled Databricks PySpark Developer to join our data platform implementation team. In this role, you will be responsible for designing, developing, and optimizing scalable ETL pipelines and data workflows using Databricks and Apache Spark. You will work closely with data engineers, data scientists, and BI teams to support advanced analytics and reporting requirements.",
"word_count": 52
},
{
"bullet_count": 4,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 ETL Development \u0026 Data Engineering",
"last_5_words": "knowledge sharing where required."
},
"text": "\u2022 ETL Development \u0026 Data Engineering Design, develop, and maintain scalable ETL processes using Databricks PySpark. Extract, transform, and load data from heterogeneous sources into Data Lake and Data Warehouse environments. Optimize ETL workflows for performance, scalability, and cost efficiency using Spark SQL and PySpark. Implement robust error handling, logging, and monitoring mechanisms for ETL jobs. Design and implement data solutions following Medallion Architecture (Bronze, Silver, Gold layers). Ensure data is cleansed, enriched, validated, and optimized at each layer for analytics consumption.\n\u2022 Data Pipeline Management Hands-on experience in building and managing advanced data pipelines using Databricks Workflows. Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity. Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines.\n\u2022 Data Analysis \u0026 Query Optimization Write, review, and optimize complex SQL queries for data transformation, aggregation, and analysis. Perform query tuning and performance optimization on large-scale datasets within Databricks.\n\u2022 Project Coordination \u0026 Continuous Improvement Participate in project planning, estimation, and delivery activities. Stay updated with the latest features in Databricks, Spark, and cloud data platforms, and recommend best practices. Document ETL processes, data lineage, metadata, and workflows to support data governance and compliance. Mentor junior developers and contribute to team knowledge sharing where required.",
"word_count": 309
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Databricks"
},
{
"is_primary": true,
"skill_name": "PySpark"
},
{
"is_primary": true,
"skill_name": "Apache Spark"
},
{
"is_primary": true,
"skill_name": "Spark SQL"
},
{
"is_primary": true,
"skill_name": "SQL"
},
{
"is_primary": true,
"skill_name": "ETL"
},
{
"is_primary": true,
"skill_name": "Data Lake"
},
{
"is_primary": true,
"skill_name": "Data Warehouse"
},
{
"is_primary": true,
"skill_name": "Medallion Architecture"
},
{
"is_primary": true,
"skill_name": "Databricks Workflows"
}
],
"jd_role": {
"display_name": "Databricks PySpark Developer",
"rationale": null,
"role_aliases": [
"PySpark Developer",
"ETL Developer",
"Data Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [
"Databricks",
"PySpark",
"Spark SQL"
],
"company_name": null,
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
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}
],
"experience": {
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"min": 5,
"raw": "5+ years"
},
"job_locations": [
{
"aliases": [
"Bengaluru"
],
"city": "Bangalore",
"country": "India",
"state": null,
"work_mode": "onsite"
}
],
"role": "Databricks PySpark Developer",
"role_aliases": [
"PySpark Developer",
"ETL Developer",
"Data Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Role Overview",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are looking for a",
"last_5_words": "analytics and reporting requirements."
},
"text": "We are looking for a highly skilled Databricks PySpark Developer to join our data platform implementation team. In this role, you will be responsible for designing, developing, and optimizing scalable ETL pipelines and data workflows using Databricks and Apache Spark. You will work closely with data engineers, data scientists, and BI teams to support advanced analytics and reporting requirements.",
"word_count": 52
},
{
"bullet_count": 4,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 ETL Development \u0026 Data Engineering",
"last_5_words": "knowledge sharing where required."
},
"text": "\u2022 ETL Development \u0026 Data Engineering Design, develop, and maintain scalable ETL processes using Databricks PySpark. Extract, transform, and load data from heterogeneous sources into Data Lake and Data Warehouse environments. Optimize ETL workflows for performance, scalability, and cost efficiency using Spark SQL and PySpark. Implement robust error handling, logging, and monitoring mechanisms for ETL jobs. Design and implement data solutions following Medallion Architecture (Bronze, Silver, Gold layers). Ensure data is cleansed, enriched, validated, and optimized at each layer for analytics consumption.\n\u2022 Data Pipeline Management Hands-on experience in building and managing advanced data pipelines using Databricks Workflows. Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity. Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines.\n\u2022 Data Analysis \u0026 Query Optimization Write, review, and optimize complex SQL queries for data transformation, aggregation, and analysis. Perform query tuning and performance optimization on large-scale datasets within Databricks.\n\u2022 Project Coordination \u0026 Continuous Improvement Participate in project planning, estimation, and delivery activities. Stay updated with the latest features in Databricks, Spark, and cloud data platforms, and recommend best practices. Document ETL processes, data lineage, metadata, and workflows to support data governance and compliance. Mentor junior developers and contribute to team knowledge sharing where required.",
"word_count": 309
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "c9b2985f-96f8-4315-8d64-ebc774498e2d",
"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": "Implements data transformation, cleansing, deduplication, and enrichment logic to convert raw source data into analytics-ready curated datasets.",
"sentence": "Ensure data is cleansed, enriched, validated, and optimized at each layer for analytics consumption.",
"similarity": 0.696
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines.",
"similarity": 0.6691
},
{
"kra_text": "Maintains data catalog entries, column-level data lineage, and technical documentation to support data discoverability and governance across the organization.",
"sentence": "Document ETL processes, data lineage, metadata, and workflows to support data governance and compliance.",
"similarity": 0.6656
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6769,
"slug": "data-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": "Data Analysis \u0026 Query Optimization Write, review, and optimize complex SQL queries for data transformation, aggregation, and analysis.",
"similarity": 0.5774
},
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
"sentence": "Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity.",
"similarity": 0.4976
},
{
"kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
"sentence": "Perform query tuning and performance optimization on large-scale datasets within Databricks.",
"similarity": 0.4848
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5199,
"slug": "full-stack-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity.",
"similarity": 0.554
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines.",
"similarity": 0.5081
},
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Mentor junior developers and contribute to team knowledge sharing where required.",
"similarity": 0.4943
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.5188,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "ETL Development \u0026 Data Engineering Design, develop, and maintain scalable ETL processes using Databricks PySpark.",
"similarity": 0.5113
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Data Pipeline Management Hands-on experience in building and managing advanced data pipelines using Databricks Workflows.",
"similarity": 0.4995
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Develop and maintain reliable, reusable, and scalable pipelines ensuring data quality and integrity.",
"similarity": 0.4839
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4982,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "Svelte Frontend Developer",
"kra_matches": [
{
"kra_text": "performance tuning",
"sentence": "Perform query tuning and performance optimization on large-scale datasets within Databricks.",
"similarity": 0.5384
},
{
"kra_text": "backend data integration",
"sentence": "Collaborate with cross-functional teams to translate business and analytics requirements into efficient data pipelines.",
"similarity": 0.4781
},
{
"kra_text": "backend data integration",
"sentence": "Ensure data is cleansed, enriched, validated, and optimized at each layer for analytics consumption.",
"similarity": 0.4507
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 92,
"score": 0.4891,
"slug": "svelte-frontend-developer",
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}
],
"skill_match_roles": [
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{
"display_name": "Pega Developer",
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"matched_count": 1,
"matched_skills": [
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"role_id": 24,
"score": 0.1,
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}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
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"matched_count": null,
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},
"confidence": 1.0,
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"matched_kras": [],
"matched_skills": [],
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"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 0.20 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 226,
"centroid_updated": true,
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"new_kra_attached": null,
"new_skills_attached": [
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"is_primary": true,
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},
{
"is_primary": true,
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"role_display_name": "Data Engineer",
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},
{
"is_primary": true,
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},
{
"is_primary": true,
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},
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},
{
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},
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}
],
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}
}
API 2 — extract-details
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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 Lake",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Data Lake",
"matched_via": "embedding_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": "Data Warehouse",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Databases",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-warehouse",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Medallion Architecture",
"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": "medallion-architecture",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Databricks Workflows",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "databricks-workflows",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Spark SQL",
"ETL",
"Data Warehouse",
"Medallion Architecture",
"Databricks Workflows"
]
}
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 0.20 does not contradict",
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Databricks",
"tag": "in_db"
},
{
"skill": "PySpark",
"tag": "in_db"
},
{
"skill": "Apache Spark",
"tag": "in_db"
},
{
"skill": "Spark SQL",
"tag": "new"
},
{
"skill": "SQL",
"tag": "in_db"
},
{
"skill": "ETL",
"tag": "new"
},
{
"skill": "Data Lake",
"tag": "in_db"
},
{
"skill": "Data Warehouse",
"tag": "new"
},
{
"skill": "Medallion Architecture",
"tag": "new"
},
{
"skill": "Databricks Workflows",
"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": "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": "Databricks",
"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": 1202,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "PySpark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ETL and ELT Tooling",
"id": 24,
"rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
"slug": "etl-and-elt-tooling",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Apache Spark",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1350,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Pega Programming Languages \u0026 DSLs",
"id": 267,
"rationale": "Programming languages and domain-specific languages used in Pega development.",
"slug": "pega-programming-languages-dsls",
"source": "db"
},
"dimension_id": 267,
"input_skill": "SQL",
"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": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "SQL",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 101,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 2,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Storage and Data Services",
"id": 144,
"rationale": "Cloud-native storage and managed data services used to place workloads, choose durability tiers, and define platform boundaries. This is a coherent cluster because architects evaluate storage fit, access patterns, and managed service tradeoffs.",
"slug": "cloud-storage-and-data-services",
"source": "db"
},
"dimension_id": 144,
"input_skill": "Data Lake",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"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": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"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 Lake",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 3
},
"planner_output": null,
"run_id": "c9b2985f-96f8-4315-8d64-ebc774498e2d"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.