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sai venkata
Senior Data Engineer specializing in cloud lakehouse and real-time streaming pipelines
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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.
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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
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“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”
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