I have extensive experience with ETL processes, having worked on designing, developing, and optimizing data pipelines. I have used tools like Informatica, Talend, and SSIS to extract, transform, and load data from various sources into data warehouses. My focus has always been on ensuring data accuracy, performance, and scalability while adhering to best practices and maintaining documentation for seamless collaboration with teams.

I ensure data quality by implementing validation rules, data profiling, and cleansing processes during ETL. I also use constraints, indexes, and referential integrity in the database schema. Regular audits, automated testing, and monitoring tools help identify anomalies. Collaboration with stakeholders to define data standards and documentation ensures consistency and transparency across the data warehouse.

A star schema is a denormalized data model with a central fact table connected to dimension tables, simplifying queries and improving performance. A snowflake schema is a normalized version where dimension tables are further broken into sub-dimensions, reducing redundancy but increasing complexity. I choose the schema based on query performance needs, storage constraints, and the balance between simplicity and normalization.

I optimize performance by indexing key columns, partitioning large tables, and using materialized views. I also analyze query execution plans to identify bottlenecks and rewrite inefficient queries. Regularly updating statistics and leveraging caching mechanisms further enhance performance. Collaboration with database administrators ensures proper resource allocation and system configuration for optimal performance.

I start by identifying the root cause through data profiling and tracing the data flow. I then collaborate with stakeholders to validate the discrepancies and implement corrective measures, such as adjusting ETL logic or updating source data. I document the issue and resolution process to prevent recurrence and ensure transparency. Continuous monitoring and automated alerts help detect discrepancies early, minimizing their impact.