• Led project planning and execution, establishing clear goals and milestones to align with organizational priorities.• Managed a team of 8 engineers, coordinating project tasks, monitoring progress, and delivering successful outcomes within timelines. • Tracked performance metrics across all projects, increasing team productivity by 25% while ensuring timely project delivery.• Directed multiple projects involving data governance, analytics, and cloud migrations, achieving consistent quality and operational improvements.• Designed and implemented Azure Data Factory pipelines, enabling seamless integration and transformation of over 10TB of healthcare data from multiple sources, including Azure SQL, Blob Storage, and NoSQL databases.• Managed and processed HIPAA-compliant EDI transaction sets (834, 835, 837, 270, 271), reducing processing errors by 20% through automated validation and reconciliation workflows.• Built and optimized dimensional models (Star and Snowflake schemas) in Snowflake and Azure Synapse Analytics, improving query performance by 30% for clinical analytics and reporting.• Developed PySpark scripts for real-time transformations, reducing data processing time by 40% across large datasets.• Created and maintained Power BI dashboards with over 50 KPIs, delivering real-time insights into patient care and operational efficiency.• Collaborated with cross-functional teams to support analytics projects, contributing to a 15% improvement in operational decision-making efficiency.

• Designed and automated ETL workflows using Apache Airflow, SSIS, and AWS Glue, reducing manual intervention by60% and ensuring timely data delivery across multiple systems.• Migrated legacy on-premise data pipelines to AWS cloud, leveraging S3, Redshift, and EMR, achieving 25% cost savingsthrough optimized resource utilization.• Engineered distributed data processing workflows using Apache Spark and Hadoop, processing over 15TB of data daily to support advanced analytics initiatives.• Integrated data from NoSQL databases (MongoDB, Cassandra, HBase) into centralized data lakes, enhancing data availability for analytics teams by 35%.• Developed advanced Tableau dashboards and Looker visualizations, providing actionable insights that drove a 20% increase in operational efficiency for financial clients.• Automated ETL validation processes using Python, achieving 99.9% accuracy in data migration and reducing errors in production environments.

• Built and maintained data pipelines using Kafka Streams and Spark Streaming, enabling real-time processing of 5+ million records daily for financial and IT clients.• Designed and implemented data warehouses on Snowflake and Google BigQuery, reducing data retrieval times by 40% and supporting advanced reporting capabilities.• Automated ETL processes using Informatica PowerCenter and dbt, improving ETL efficiency by 50% and reducing deployment times from weeks to days.• Utilized Azure Synapse Analytics, Databricks, and HDInsight for big data batch processing and real-time analytics, leading to a 20% increase in project delivery speed.• Improved data governance by implementing Collibra and Alation, achieving a 30% reduction in compliance risks and ensuring adherence to global data privacy regulations.• Created predictive analytics models using Python, TensorFlow, and Scikit-learn, supporting financial risk mitigation strategies and saving over $1 million annually.• Led a team to implement scalable ETL pipelines across multiple databases (Oracle, SQL Server, Hive, MongoDB), increasing data accuracy and reducing latency by 35%.