Pipeline run
212cb41a-60b7-467d-a650-070a5dcee4fa
Pipeline LLM cost (USD)
API 1: $0.0038
API 2: $0.0000
API 3: $0.0000
Total: $0.0038
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 pipeline development
Build and optimize Databricks PySpark ETL pipelines and workflows, moving data from source systems into Data Lake/Warehouse layers with cleansing, validation, and Medallion Architecture. Also tune SQL/Spark performance, add logging/monitoring, and document lineage/governance artifacts.
"designing, developing, and optimizing scalable ETL pipelines and data workflows using Databricks and Apache Spark"
Tech stack maturity
Modern Cloud Native
Apache Spark, Databricks, and SQL query optimization are strongly associated with cloud-native data platforms and modern distributed analytics stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 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):
—
Evidence — skills matched in JD (16)
Databricks
PySpark
Apache Spark
Spark SQL
SQL
ETL
Data Lake
Data Warehouse
Medallion Architecture
Databricks Workflows
Data Pipelines
Data Quality
Query Optimization
Performance Optimization
Data Governance
Compliance
Skill cluster (4 dimension groups, role-scoped)
ETL and ELT Tooling
Apache Spark
Performance and Cost Optimization
Query Optimization
Programming Languages for Data Work
SQL
Cross-cutting / unaligned
Databricks
PySpark
Spark SQL
ETL
Data Lake
Data Warehouse
Medallion Architecture
Databricks Workflows
Data Pipelines
Data Quality
Performance Optimization
Data Governance
Compliance
Show KRA description ↓
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.
• 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.
Signals
Skill
data-engineer
0.21
Alias
data-engineer
1.00
KRA
data-engineer
0.68
Post-classification
Centroidupdated · n=213
Alias collision log—
New-role queue—
New skills captured12
New KRA captured—
Captured for admin review
PySpark
primary
↔
Data Engineer
pending
Spark SQL
primary
↔
Data Engineer
pending
ETL
primary
↔
Data Engineer
pending
Data Lake
primary
↔
Data Engineer
pending
Data Warehouse
primary
↔
Data Engineer
pending
Medallion Architecture
primary
↔
Data Engineer
pending
Databricks Workflows
primary
↔
Data Engineer
pending
Data Pipelines
primary
↔
Data Engineer
pending
Data Quality
primary
↔
Data Engineer
pending
Performance Optimization
primary
↔
Data Engineer
pending
Data Governance
↔
Data Engineer
pending
Compliance
↔
Data Engineer
pending
Status:
extract_from_jd_done
Created: 2026-05-27T14:47:32.210817Z
Updated: 2026-06-12T17:18:05.066587Z
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
No chosen role stored for this run.
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.
Databricks
Primary
No API 2 row (run stopped after API 1 or history missing)
PySpark
Primary
No API 2 row (run stopped after API 1 or history missing)
Apache Spark
Primary
No API 2 row (run stopped after API 1 or history missing)
Spark SQL
Primary
No API 2 row (run stopped after API 1 or history missing)
SQL
Primary
No API 2 row (run stopped after API 1 or history missing)
ETL
Primary
No API 2 row (run stopped after API 1 or history missing)
Data Lake
Primary
No API 2 row (run stopped after API 1 or history missing)
Data Warehouse
Primary
No API 2 row (run stopped after API 1 or history missing)
Medallion Architecture
Primary
No API 2 row (run stopped after API 1 or history missing)
Databricks Workflows
Primary
No API 2 row (run stopped after API 1 or history missing)
Data Pipelines
Primary
No API 2 row (run stopped after API 1 or history missing)
Data Quality
Primary
No API 2 row (run stopped after API 1 or history missing)
Query Optimization
Primary
No API 2 row (run stopped after API 1 or history missing)
Performance Optimization
Primary
No API 2 row (run stopped after API 1 or history missing)
Data Governance
Secondary
No API 2 row (run stopped after API 1 or history missing)
Compliance
Secondary
No API 2 row (run stopped after API 1 or history missing)
Library artifacts (this run)
No artifact rows for this run.
nano JD Parser — gpt-4.1-nano click to toggle
RoleDatabricks PySpark Developer
Experience5+ years
DomainIT Services & Consulting
Location
Bangalore, India
(onsite)
JD type
pass
Certifications
Databricks
PySpark
Spark SQL
Show raw JSON
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API 1 — extract-from-jd click to toggle
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{
"is_primary": true,
"queue_id": 10996,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Pipelines",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 10997,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Quality",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 10998,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Performance Optimization",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 10999,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Data Governance",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 11000,
"role_display_name": "Data Engineer",
"role_slug": "data-engineer",
"skill_name": "Compliance",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{}
API 3 — final-role-output
{}
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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