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Sai Gowtham Madaka
Mid-level Data Engineer specializing in streaming and cloud data platforms for financial services
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Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.
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Key Strengths
- Owned end-to-end transactional data pipeline for fraud detection/reporting (ingestion → validation → PySpark transforms → warehouse load)
- Built data quality gates (schema/null/row-count/anomaly checks) to prevent bad data from propagating
- Improved pipeline reliability via Airflow orchestration with task dependencies and retries
- Implemented logging/monitoring with operational metrics (record counts, processing time) for faster failure/slowdown diagnosis
- Designed idempotent jobs to enable safe retries without duplicates/corruption
- Designed external data collection pipeline with anti-bot measures (throttling, delays, rotating headers) and schema-change quarantine handling
- Shipped a Flask-based data API with SQL query optimization, input/output validation, and endpoint versioning
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