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priya kotha
Mid-level Data Engineer specializing in real-time pipelines across FinTech and Healthcare
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Data engineer at Plaid who built greenfield, end-to-end real-time transaction pipelines and FastAPI data services for fraud detection and analytics, handling millions of events per day. Strong focus on reliability and data integrity via Great Expectations validation, Airflow-based monitoring/SLAs, quarantine/staging patterns, and robust external data ingestion with schema versioning and backfills (reported 50% fewer anomalies and ~40% fewer failures).
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Key Strengths
- Owned end-to-end real-time financial transaction pipeline (ingestion to serving) processing millions of events daily
- Implemented layered data quality checks with Great Expectations (schema, null thresholds, duplicates, business rules)
- Built monitoring/alerting around SLAs, data freshness, and failure rates using Airflow and logging
- Designed quarantine/staging mechanisms to prevent bad data from propagating downstream
- Reduced data anomalies ~50% and achieved ~90% pipeline reliability for fraud detection use cases
- Built external data ingestion system with retries/exponential backoff/rate limiting and schema versioning
- Reduced pipeline failures ~40% via reliability improvements and backfill/reprocessing workflows
- Shipped low-latency FastAPI backend service consuming Kafka streams with Redshift-backed queries and in-memory caching
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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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