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Snehitha Borra
Mid-level Data Engineer specializing in cloud data platforms and big data pipelines
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Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.
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Key Strengths
- End-to-end ownership of production data pipelines (design, implementation, orchestration, monitoring, support)
- Built validation directly into pipelines (not as an afterthought) to improve data quality and reliability
- Implemented schema validation and schema-drift detection to catch upstream changes early and alert/handle dynamically
- Reduced downstream impact and manual fixes via deduplication and business-rule validations in transformation layer
- Metadata-driven transformation framework in Spark/Databricks to reuse common rules and avoid one-off scripts
- Operational rigor: monitoring/alerts, retries, safe reruns without duplicates, and backfill-safe reprocessing
- Pragmatic delivery in ambiguous environments using familiar tooling and iterative hardening post-launch
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