sai venkata

About

Senior data engineer with experience in both healthcare (CVS Health) and financial services (Bank of America), building large-scale Azure lakehouse pipelines (30+ EHR sources, ~5TB) and real-time streaming services (Event Hubs/Kafka) for patient vitals. Strong focus on reliability and data quality (Great Expectations, monitoring/alerting, schema drift automation), with measurable outcomes like 50% runtime reduction and 99%+ uptime for regulatory reporting pipelines.

Experience

Education

Key Strengths

• Owned end-to-end healthcare data pipelines at ~5TB scale (ingestion to serving) on Azure
• Implemented automated data quality validation (Great Expectations + PySpark) with anomaly logging/alerting
• Handled schema drift across 30+ EHR sources via metadata-driven ingestion and schema handling framework
• Spark optimization (caching, broadcast joins, parameterized jobs) cutting runtime ~50%
• Built external data ingestion for financial/regulatory sources with Datadog monitoring and automated retries; achieved 99%+ uptime
• Designed real-time streaming service (Event Hubs/Kafka + Structured Streaming) with sub-second latency and exactly-once processing
• Ensured backward compatibility via schema registry and schema evolution practices
• Stood up early-stage real-time patient monitoring platform with CI/CD, observability, and incident response from scratch