I am a Snowflake Data Engineer with over 6 years of professional experience in data engineering, healthcare claims auditing, and analytics reporting. I have more than 4 years of hands-on experience with Snowflake and over 2 years of experience building dbt models and Apache Airflow pipelines on AWS.
I specialize in designing and maintaining ETL/ELT pipelines for structured and semi-structured data. My technical strengths include SQL, Python, Snowflake, dbt, Apache Airflow, AWS S3, AWS Glue, AWS Lambda, Redshift, data warehousing, and dimensional data modeling.
In my recent role, I developed data platforms for automotive telemetry analytics, including vehicle, trip, diagnostic, and sensor datasets. I built dbt fact and dimension models, orchestrated Airflow workflows, and implemented near-real-time ingestion using Snowpipe, Streams, and Tasks.
I have experience improving warehouse performance and managing cloud data costs through query optimization, clustering, warehouse sizing, materialized views, and resource monitors. I also build data-quality, reconciliation, and validation checks to ensure reporting datasets are accurate and reliable.
Earlier in my career, I worked on US healthcare claims reporting and audit operations. I helped modernize manual CSV-based claims reporting by implementing Amazon S3-to-Snowflake pipelines, tracking claim status changes, and delivering curated monthly datasets for Power BI dashboards.
I am an immediate joiner and am seeking opportunities where I can contribute my Snowflake, dbt, SQL, Python, Airflow, and AWS expertise to scalable data engineering and analytics initiatives.
