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Chinnolla Sathvik Reddy
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Houston, Texas, United States
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Data EngineerCenteneJul 2024 - Current (2 years 4 months)As a Data Engineer, I built scalable hybrid data architecture in Azure to support ML/AI workflows, significantly increasing data reliability and enabling the processing of petabyte-scale healthcare datasets from over 10+ sources. I reduced data processing time by 30% by optimizing Spark jobs (PySpark/Scala) in Databricks, which improved model training throughput and downstream analytics readiness. Additionally, I engineered custom ETL pipelines using Python and SQL, integrating with enterprise data warehouses to enhance data accuracy and reduce transformation errors by 25%. I cut manual provisioning effort by 50% by automating cloud infrastructure and workflow orchestration in Python, accelerating deployment cycles and enforcing CI/CD best- Data EngineerPaychexNov 2023 - Jun 2024 (8 months)• Improved pipeline stability by 25% by managing and optimizing high-volume AWS environments using proactive capacity planning and continuous monitoring. • Reduced deployment errors by 40% through automation of data pipeline deployments and infrastructure tasks with Python, enhancing CI/CD pipeline reliability. • Migrated 20+ legacy pipelines from on-prem to AWS S3 using Databricks and Python, enabling scalable, cost-effective ETL with robust physical data modeling. • Engineered PySpark/Scala applications for payroll data validation, transformation, and cleansing, ensuring compliance readiness and accelerating model training cycles. • Cut incident resolution time by 30% by identifying and fixing system defects and data anomalies, drivi
- Data EngineervmwareJun 2022 - Jul 2023 (1 year 2 months)• Reduced downstream data errors by 30% by building and maintaining complex ETL pipelines using Python, Spark, and IBM DataStage, embedding rigorous data quality checks. • Improved ingestion efficiency by 60% through automation of data collection from external sources to AWS S3, enabling scalable integration and reducing manual intervention. • Optimized performance of 20+ analytical workflows by managing data movement from AWS S3 into Snowflake and DynamoDB, supporting a high-throughput enterprise data warehouse. • Delivered ML-ready datasets via robust Python and Spark (Scala) pipelines, ensuring consistent and timely data availability for performance optimization across business units. • Enabled seamless data migration to AWS Redshif
- Data EngineerVeriskMar 2020 - May 2022 (2 years 3 months)• Boosted data processing performance by 35% by developing and optimizing complex data architectures and pipelines using advanced SQL and Python, aligned with enterprise data warehouse goals. • Reduced data discrepancies by 40% through implementation of automated quality checks and reconciliation across structured (SQL) and NoSQL (MongoDB, Elasticsearch) sources. • Unified 10+ disparate data sources into machine-readable datasets by engineering Spark-based enrichment and transformation workflows, streamlining analytics readiness. • Cut manual intervention by 50% by automating batch processing and monitoring tasks with UNIX/Linux scripting, increasing data flow consistency and operational reliability. • Enhanced platform scalability and
Education
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- Master of ScienceSaint Peter's University
- MMicrosoft certified - azure data fundamentals
- CCertified AWS Data Engineer.