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Sai Kavyusha Ponnagant
Mid-level Data Engineer specializing in cloud data pipelines and financial services warehousing
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About
Data engineer (Charles Schwab) who took ownership of an unstable, ambiguous nightly financial data pipeline and rebuilt it into a reliable, incremental AWS Glue/Airflow/Redshift system feeding Power BI. Created a custom Python data-quality framework with hard-stop gating and schema drift detection, improving integrity (99.9%), cutting runtime (~20%), and reducing incidents/tickets (35% fewer schema-related dashboard incidents; 30% fewer investigations).
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Key Strengths
- Owned end-to-end nightly batch pipeline (ingestion to Power BI serving) with millions of records/night
- Built Python-based data quality framework that stopped bad loads; caught 14 bad loads in 6 months
- Incremental ingestion via timestamp watermarking delivering ~20% runtime reduction
- Designed raw/staging/curated Redshift architecture with auditability and schema isolation; reduced schema-related dashboard incidents by 35%
- Implemented SCD Type 2 and deduplication logic (ROW_NUMBER) for accurate dimensional history and clean facts
- Redshift performance optimization (dist keys, compound sort keys) and WLM separation to prevent ETL/analyst contention
- Strong operational rigor: Airflow retries/alerts, CloudWatch monitoring, daily pipeline health summaries, rapid incident resolution
- Reduced downstream support burden (30% reduction in data investigation tickets) while maintaining 99.9% data integrity over 6 months
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