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.