Area(s) of responsibility

Key Responsibilities

1. 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

2. 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)
🏢 Birlasoft
📍 Pune