Sasidhar Mopuru

Data Engineer, Management and Governance Analyst

• Delivered 90+ production PySpark ETL jobs across 4 supply-chain sub-domains, contributing to 50+ data products through Python development, JSON configuration, and DDL development.
• Led 12 change requests across data products and completed tag validations in a single day by comparing design documents, source messages, and data across staging, active, and history layers.
• Worked in a 7-member E2E supply-chain data engineering team (1 lead, 1 tester, 5 DEs) covering site, supplier, item, and product sub-domains; primarily owned site and supplier pipelines while supporting item and product live monitoring.
• Managed full lifecycle of data products: PySpark/Python main scripts, JSON configs, unit tests, SingleStore tables, Delta Lake lakehouse tables (with catalog and schemas), Control-M scheduling, IOMate job orchestration, and Acceldata monitoring across DEV, SIT, and PROD.
• Reduced deployment time by 40% by developing configuration-driven pipeline definitions and reusable Python utilities used by the data platform and consumed by existing CI/CD workflows.
• Achieved 99.5% pipeline uptime through modular PySpark pipelines with error handling, retry logic, schema validation, and data quality checks.
• Maintained 95%+ overall test coverage across 60+ pytest suites with mocked components and integration patterns.
• Improved data processing latency by 30% through partitioning, caching, and performance tuning on production pipelines.
• Owned end-to-end data quality and platform validation for 3–4 sprint releases, validating schemas, tags, record counts, primary keys, business hash keys, and duplicate records across source-to-target data layers, and prepared SQL-based reconciliation evidence for clean production sign-off.
• Worked with Kafka-based data pipelines and validation workflows, DDL scripts, and validation/reconciliation queries for real-time data processing, and monitored and troubleshot production pipelines.
• Used Jira dashboards to track sprint tasks, incidents, and maintenance activities for data products across the supply-chain platform.
• Developed data products from design documents and built downstream datasets from source schemas, applying transformation queries when multiple source data products feed a single dataset; maintained per-schema exception tables to capture invalid records with target table reference, error log, and timestamp.