Lead Data Engineer

- Designed and implemented enterprise-scale data engineering solutions using Databricks, Microsoft Fabric, Delta Lake, PySpark, SQL, and Power BI to enable high-performance analytics. - Led data platform modernization by migrating legacy reporting solutions to Databricks Lakehouse, implementing Medallion Architecture and dimensional modeling best practices. - Improved platform performance and cost efficiency through Spark optimization, incremental data processing, and modernization initiatives, significantly reducing execution times and costs. - Evaluated and introduced modern data engineering practices, including Databricks Change Data Feed (CDF), Databricks Asset Bundles (DAB), code maintainability, and developer productivity. - Mentored a team of 4 Data Engineers, guiding project planning, technical implementation, and smooth project transitions.

- Architected and delivered enterprise data engineering and analytics solutions using Azure Data Factory, Snowflake, SQL, and Power BI, supporting organization-wide decision-making. - Collaborated with business and technical stakeholders, including PMO, TA, TM, and Engineering, to define and compile requirements. - Designed scalable data pipelines, dimensional models, and optimized semantic models aligned with robust business logic. - Developed secure, high-performance Power BI solutions using advanced DAX, Power Query, drill-through reporting, and Row-Level Security (RLS) for executive reporting.

- Developed and maintained big data applications using Apache Spark (Databricks) to process Bentley product usage and feature telemetry. - Designed and implemented end-to-end data pipelines, from ingesting product usage data to transforming and loading datasets into Snowflake for BI and analytics. - Collaborated with product teams to onboard new data sources and extend existing data processing workflows to support evolving business requirements. - Developed unit and integration test cases and implemented Azure DevOps CI/CD pipelines to automate the build, testing, and deployment of Databricks, Azure Data Factory, Azure Functions, and other Azure resources across multiple environments.