I start by understanding the business goals, reporting needs, and data sources, assessing data quality, formats, and accessibility. This ensures that the pipeline design aligns with both business objectives and technical constraints, whether the data comes from APIs, databases, CSVs, or external datasets like Kaggle or Github.

I design the data flow architecture using ADF, Databricks, and Synapse, typically following a medallion architecture (Bronze → Silver → Gold). Decisions about batch processing, incremental loading with CDC(thankfully apply_changes API), and file formats like Delta, Parquet, or CSV are made to optimize performance and maintainability.

I develop ETL/ELT pipelines in ADF and Databricks, applying transformations, aggregations, and data cleaning using PySpark or SQL. Delta Lake is used to provide ACID-compliant, versioned storage, ensuring reliable and reproducible datasets.

I implement data governance with Unity Catalog and RBAC, ensuring secure and compliant access. While validating data quality through incremental loads and checks.

Curated datasets are delivered to Synapse, SQL warehouses, or Power BI, making them ready for analytics and reporting. I create reusable views or tables to simplify access for analysts and business users.