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UMESH KAMISETTY
Mid-level Data Engineer specializing in cloud lakehouse and streaming platforms
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Data engineer focused on building production-grade pipelines on AWS (Kafka/Kinesis/Glue/S3) through to curated serving layers in Snowflake and Delta Lake. Emphasizes automated data quality validation (PySpark + CI/CD), modular dbt transformations for analytics (customer spending, risk metrics), and operational reliability with CloudWatch and DLQs; data consumed by BI tools and ML pipelines for fraud detection and risk analytics.
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Key Strengths
- End-to-end data pipeline ownership (ingestion to serving) on AWS + Snowflake/Delta Lake
- Strong data quality/validation implementation in PySpark (schema, nulls, duplicates, referential integrity, business rules)
- Production analytics transformations using modular dbt models in Snowflake
- Operational reliability practices (CloudWatch monitoring, DLQs for retries/failures, CI/CD-integrated checks)
- Performance optimization for data access (query optimization, caching, partitioning)
- Developer enablement via clear API contracts, documentation, and reusable data models
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