Senior Data Engineer

Develop and maintain scalable data transformation workflows that convert operational and business data into reliable, analytics-ready datasets. Use SQL, Python, Spark, PySpark, and Databricks-oriented patterns to create reusable logic, standardize business rules, and process growing datasets efficiently.

Design dimensional models and layered Bronze, Silver, and Gold data processing patterns. Implement ETL/ELT workflows, incremental processing, data-quality validation, reconciliation, exception handling, logging, and monitoring. Apply Git, pull requests, code reviews, and CI/CD practices while partnering with analysts and stakeholders on data models, documentation, and issue resolution.

Built and maintained SQL- and Python-based data preparation workflows for banking and transaction datasets used in operational analysis and management reporting. Integrated relational sources and structured files, applying standardization, transformation, and validation before publishing analytical datasets.

Created and optimized complex SQL queries, joins, aggregations, and reusable transformations. Supported data warehouse modeling, source-to-target reconciliation, data-quality checks, and investigation of missing, duplicate, or inconsistent records. Worked with Snowflake and SQL Server, automated recurring preparation tasks, and maintained transformation, mapping, and validation documentation using Git-based development practices.

Master of Science in Business Analytics, University of Massachusetts, Amherst, Massachusetts.