Databricks - Technical Specialist-Data Engg

Area(s) of responsibility

• Role Summary We are looking for a highly skilled Databricks Data Engineer with strong hands on expertise in Databricks, PySpark, ADF, and SQL. The candidate will be responsible for architecting, developing, and optimizing modern data platforms and analytical solutions on Azure. The role requires strong experience building enterprise-grade data pipelines, enabling ingestion from diverse sources, implementing complex transformations, and supporting scalable analytics initiatives.
• 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 Databicks Genie.
• 2.Design and implement curated data models in Gold layer aligned to business use cases 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. 3. Data Governance & Quality Implement data quality checks, audit mechanisms, and validation frameworks to ensure data accuracy and consistency in Unity Catalog. Enforce: 1. Unity Catalog standards 2. naming conventions, metadata policies 3. access controls (RBAC/ABAC) 4. Handle changes such as: -Column derivation logic changes (not just schema changes) -Backward compatibility and impact analysis 5. Partner with business / data stewards to: -Define business rules -Validate metrics and edge cases