I am a senior data engineer with 7+ years of experience building scalable data platforms, cloud-native pipelines, and analytics solutions across healthcare, pharmaceutical, supply chain, and automotive domains.
I specialize in designing enterprise data lakes, lakehouse architectures, ETL/ELT frameworks, and dimensional data models using Python, PySpark, SQL, Azure Databricks, Snowflake, Azure Data Factory, and AWS data services.
I have worked extensively with large-scale structured and semi-structured datasets, focusing on data quality, performance optimization, governance, and reliable delivery of trusted analytics data.
In my recent roles, I have supported healthcare and supply chain operations by developing high-volume pipelines, curated datasets, and reporting layers that power business intelligence, operational analytics, and regulatory reporting.
I also have experience enabling machine learning workflows by preparing feature engineering pipelines, model-ready datasets, and supporting experiment tracking with MLflow.
I work closely with cross-functional teams in Agile environments and take pride in improving pipeline reliability, reducing latency, and building secure, scalable, and maintainable data solutions.
My background includes both modern cloud data engineering and earlier SQL/ETL development, giving me a strong foundation across the full data lifecycle from ingestion and transformation to reporting and production support.
