Azure Databricks Developer

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.