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Sheshikanth Pothuganti
Mid-level Data Engineer specializing in real-time streaming and cloud data platforms
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Data engineer with Wells Fargo experience owning an end-to-end lakehouse ETL pipeline on Databricks/Azure Data Factory, processing ~480GB daily and implementing robust data quality/reconciliation across 40+ tables to reach ~99.3% reliability. Strong in performance optimization (cut runtime 5.5h→3.8h), CI/CD and monitoring, and resilient external/API ingestion with retries, schema validation, and backfills.
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Key Strengths
- Owned end-to-end ETL pipeline processing ~480 GB/day across 40+ tables
- Improved pipeline reliability to ~99.3% via data quality checks and reconciliation
- Reduced batch runtime from 5.5 hours to 3.8 hours through PySpark performance optimization
- Built automated validation frameworks and alerting to catch data issues early
- Designed resilient external data ingestion with retries (exponential backoff), schema validation, and backfill/reprocessing logic
- Set up CI/CD and monitoring for lakehouse pipelines in an ambiguous early-stage environment
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