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Saiteja Gaddam
Mid-Level Data Engineer specializing in cloud data platforms and streaming analytics
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About
Data engineer (Intuit) who owned an end-to-end telemetry and subscription analytics platform processing ~22M events/day, built on Kinesis/S3/Glue/Spark/Airflow/Redshift. Strong focus on reliability and data quality (schema drift controls, quarantine layers, idempotent reruns) and performance tuning, achieving a reporting latency reduction from ~15 minutes to under 4 minutes while enabling revenue and churn analytics for business teams.
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Key Strengths
- Owned end-to-end telemetry + subscription analytics pipeline processing ~22M events/day
- Designed streaming ingestion + S3 data lake with raw/validated/curated zones to enforce data quality
- Implemented robust data quality controls (schema enforcement, null checks, anomaly detection) with quarantine routing to prevent downstream corruption
- Orchestrated 120+ Airflow workflows with SLA monitoring, retries, and CloudWatch alerting to reduce manual intervention
- Optimized Spark jobs and Redshift (dist/sort keys) to cut reporting latency from ~15 minutes to <4 minutes
- Built curated warehouse service layer (models/views) to standardize business logic for revenue attribution and user activity metrics
- Handled schema drift and upstream inconsistencies with flexible parsing, validation layers, and safe backfill/reprocessing patterns (idempotency, partitioning, checkpointing)
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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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