Area(s) of responsibility
Key Responsibilities
Data Engineering Execution

Build and maintain ingestion frameworks (ADF / Databricks / Spark)
Implement Bronze -> Silver transformations aligned to architecture
Architect data quality checks, schema validation, and contract rules
Build and optimize robust, high-throughput ELT/ETL pipelines, enabling ingestion, transformation, and curation of structured, semi structured, and unstructured data.
Integrate data from multiple on premise and cloud based systems, APIs, and third-party sources.
Implement complex transformations using PySpark, ensuring performance efficiency and code modularity.
Build orchestration workflows in ADF, including pipelines, triggers, linked services, integration runtimes, and parameterized datasets.
Familiar with using Databricks Genie.
Build: Dimensional models (star/snowflake schemas), Fact tables, dimensions, surrogate keys, SCD handling




Translate Silver datasets into: Analytics-ready models, Consistent KPI definitions and business logic
Ensure: Consistency across domains (common dimensions, conformed models), Reusability and scalability of models
Databricks & PySpark Engineering

Develop scalable transformation scripts using PySpark on Databricks, applying advanced optimizations like caching, partitioning, and Delta Lake capabilities.
Implement Delta Lake features—ACID transactions, schema enforcement, schema evolution, and time travel—across the data lifecycle.
Perform performance tuning, handling bottlenecks related to cluster configuration, shuffle operations, joins, and parallelization.
Collaborate with platform teams to manage Databricks clusters, jobs, notebooks, and CI/CD integrations.

📌 Azure Databricks Developer Pune (India)
🏢 Birlasoft
📍 India