Uday Kiran Irukula

• Designed, built, and maintained scalable Spark data ingestion pipelines processing 500GB+ geospatial datasets from multiple external vendors using Scala, Python, and SQL, ensuring reliable extraction, transformation, and loading (ETL). • Integrated large, complex JSON datasets by implementing schema standardization, metadata validation, and geospatial integrity checks to meet business and technical requirements. • Developed robust data quality frameworks (schema validation, connectivity checks, anomaly detection) using Python and SQL-based validation rules, reducing downstream data issues by 30%. • Migrated legacy Java-based workflows to distributed Spark pipelines using Scala and Python, improving pro- cessing performance by 40% and enhancing pipeline scalability and maintainability. • Optimized data pipelines using partitioning, caching, execution tuning, and SQL query optimization tech- niques to improve reliability and runtime performance across cloud environments. • Automated pipeline orchestration using webhook-based triggers and Python utilities, reducing manual interven- tion by 80%. • Collaborated with analytics and platform teams to troubleshoot SQL query performance, Spark jobs, and data anomalies, performing deep-dive root cause analysis.

• Designed and implemented end-to-end ETL pipelines for enterprise-scale analytics use cases with supplemental data transformations using Python, Scala, Spark and SQL. • Wrote optimized SQL queries for aggregations, data profiling, and view creation. • Built automated monitoring, logging, and error-handling mechanisms, reducing pipeline downtime by 25%.