About
Highly accomplished Data Engineer with over 6 years of hands-on experience in designing, building, and optimizing large-scale data processing systems across financial, healthcare, and retail domains. Adept at architecting and implementing robust ETL pipelines, data lake solutions, and cloud-native data platforms using tools like AWS Glue, EMR, Redshift, Azure Data Factory, and Databricks. Demonstrates strong expertise in Python, SQL, and PySpark, enabling efficient data ingestion, transformation, and analytics automation. Skilled in integrating streaming and batch data sources using technologies such as Kafka, Kinesis, and Airflow, ensuring reliable and timely delivery of enterprise data. Experienced in managing data quality, lineage, and governance frameworks, aligning with compliance standards in both financial and healthcare sectors. Known for a deep understanding of healthcare payer systems, claims data structures, and regulatory data flows, alongside strong contributions to risk, fraud detection, and reporting analytics within the banking domain. Passionate about building scalable and reusable data frameworks, optimizing pipeline performance, and promoting data reliability through automated testing and monitoring. Adept at collaborating with cross-functional teams, mentoring junior engineers, and translating complex business requirements into practical, data-driven solutions. Committed to continuous learning, innovation, and delivering high-quality, production-grade data engineering solutions that empower business decision-making and analytics maturity.
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