Hari Chandana Bandreddi

About

Data engineer who has owned high-volume production pipelines end-to-end (200–300 GB/day) on AWS, implementing strong data quality/observability and achieving 99.9% reliability while cutting data issues ~33%. Also built a large-scale external data collection system ingesting millions of records/day with anti-bot/rate-limit handling and backfill tooling, and shipped a versioned REST service exposing curated Snowflake data to downstream teams.

Key Strengths

• Owned end-to-end data pipelines ingesting 200–300 GB/day into AWS S3
• Implemented robust data quality framework (schema/null/duplicate/row-count checks) with alerting
• Improved pipeline reliability to 99.9%
• Reduced data quality issues by ~33%
• Handled late-arriving/out-of-order data using watermarking and SQL window-function deduplication
• Optimized large-scale joins with repartitioning/broadcast joins and SQL rewrites (runtime reduced ~30%)
• Built external data collection pipeline ingesting millions of records/day with checkpointing and exponential backoff
• Designed anti-bot/rate-limit mitigations (throttling, header rotation, staggered scheduling)
• Shipped a REST data service over Snowflake with caching, pagination, versioned endpoints, and documentation
• Delivered MVP in ambiguous early-stage environment with managed services, CI/CD, observability, and backfill recovery

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