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srilekha pothula
Mid-level Data Engineer specializing in cloud data pipelines for healthcare and financial services
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Data engineer with ~4 years of experience (Cigna) building and operating Azure Data Factory pipelines for healthcare claims/member/provider data at 2–3M records/day. Emphasizes reliability and downstream safety via schema/data-quality validation, quarantine workflows, idempotent processing, and backfills; also improved runtime ~20% through SQL optimization and served curated datasets through versioned views and well-documented, analyst-friendly interfaces.
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Key Strengths
- Owned end-to-end Azure-based healthcare data pipelines (ingestion to serving) at ~2–3M records/day
- Improved pipeline execution time ~20% via SQL optimization
- Reduced duplicate/invalid records through automated data validation and quality checks (SQL/Python)
- Designed robust external data ingestion with monitoring, retries, schema validation, and quarantine/staging to protect downstream systems
- Implemented idempotent processing and reusable backfill pipelines to safely rerun/recover data
- Delivered performant, consumer-friendly data services via optimized views/tables, documentation, naming conventions, and sample queries
- Handled early-stage/ambiguous requirements by scoping with stakeholders and choosing pragmatic batch tradeoffs over streaming
- Operational ownership: monitoring dashboards/alerts and targeted reruns of only affected pipeline segments during incidents
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