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Phanideep P
Senior Data Engineer specializing in cloud lakehouse and streaming data platforms
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About
Data platform/data engineer with cross-industry experience in banking and healthcare, building cloud-native lakehouse architectures across AWS/Azure/GCP. Has owned high-volume (millions of records; TB/day) pipelines with strong data quality automation (dbt/Great Expectations), observability (Grafana/Prometheus), and real-time streaming (Kafka/Spark) for fraud monitoring; also delivered an early-stage migration from SQL Server to BigQuery with 40% batch latency reduction.
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Key Strengths
- Owned end-to-end lakehouse pipeline (ingestion, validation, transformations, serving) on AWS/Databricks/Snowflake
- Scaled pipelines processing millions of records and several TB/day with schema drift and freshness monitoring
- Implemented automated data quality and reconciliation with dbt + Great Expectations
- Performance optimization in Spark (partitioning, skew handling, caching) and Snowflake query tuning
- Built observability (Grafana/Prometheus dashboards and alerts) reducing processing failures
- Designed reliable ingestion with retries, idempotency, incremental processing, and efficient backfills
- Shipped real-time data services for fraud monitoring using Kafka + Spark Structured Streaming with offset management and exactly-once semantics
- Led ambiguous modernization/migration from on-prem SQL Server to GCP BigQuery with CI/CD and achieved 40% batch latency reduction
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