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Harshitha Parupalli
Mid-level Data Engineer specializing in multi-cloud real-time and batch data pipelines
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Data engineer with healthcare domain experience who owned 100M+ record pipelines end-to-end (Kafka/Kinesis/ADF → PySpark/dbt validation → Spark SQL transforms → Snowflake/Power BI serving). Built production-grade reliability practices (Airflow orchestration, CloudWatch/Grafana monitoring, pytest + contract/regression tests, idempotent ingestion/backfills) and delivered measurable improvements: 35% lower latency and 40% better query performance.
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Key Strengths
- Owned end-to-end healthcare data pipelines processing 100M+ records
- Implemented robust data quality framework (schema/null/range/duplicate/referential integrity checks) with dbt + PySpark
- Integrated automated testing (pytest, regression/contract tests) into production pipelines
- Improved data latency by 35% and query performance by 40%
- Built reliable ingestion systems with retries, idempotency, monitoring/alerts, and backfill capability
- Handled production incidents (schema-change breakages) via alerting, log isolation, rapid fixes, and preventive validation hardening
- Shipped versioned Python APIs on top of Snowflake with performance tuning and developer-friendly documentation
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