• Architected and maintained 150+ production-grade AWS ETL/ELT pipelines (S3, Glue, EMR, Redshift, Athena) across a medallion architecture (Bronze/Silver/Gold), processing hundreds of GBs daily with periodic TB-scale refreshes for downstream BI consumers.
• Standardized the data lake storage layer on Delta Lake on S3 as the production standard alongside Parquet — enabling ACID transactions, schema enforcement, and reliable upserts across the entire data lake.
• Designed and implemented a metadata-driven ingestion framework using AWS Glue Crawlers for automated schema discovery across Parquet, JSON, and CSV sources — reducing downstream data latency by ~30%.
• Orchestrated fault-tolerant batch ETL workflows using AWS Step Functions with retry logic and failure handling — reducing manual intervention and improving execution reliability in production.
• Developed incremental load logic (insert/update/delete) using SQL and PySpark, aligned with dimensional modeling — keeping Redshift tables current and analytically consistent.
• Established CI/CD pipelines using Jenkins and Git for version-controlled, repeatable deployments across dev, test, and production environments.
• Optimized SQL query performance by ~20–25% through partition pruning, predicate pushdown, and efficient joins in Redshift and Athena — reducing unnecessary data scans and compute overhead.
• Implemented proactive pipeline monitoring using CloudWatch logs, alerts, and dashboards — enabling early detection and resolution of production issues before downstream impact.
• Reduced compute and storage costs by optimizing S3 storage patterns, query structures, and scan footprints across Glue and Athena workloads.
• Partnered with analytics, QA, and BI teams across full Agile delivery cycles — sprint planning, backlog refinement, and production support — to ensure reliable, business-aligned data delivery.

• Architected and built a cloud data lakehouse on AWS, migrating and consolidating on-premises data sources into a scalable S3 architecture, driving infrastructure cost optimizations of £487K annually.
• Established a medallion architecture (Bronze/Silver/Gold) with Delta Lake on S3 as the storage standard, using AWS DMS for ingestion — enabling ACID transactions, schema enforcement, and reliable upserts.
• Built end-to-end data transformation and processing jobs using AWS Glue (PySpark) and Lambda, orchestrated via AWS Step Functions with retry logic and failure handling for production reliability.
• Delivered data to 6 downstream consumer systems based on business requirements — Athena (SQL querying), QuickSight (dashboards), PostgreSQL (cost-efficient results), Redshift (analytical workloads), Snowflake (project-specific), and DynamoDB — ensuring each team received fit-for-purpose data.
• Collaborated across an Agile team via sprint planning, Jira-based task management, and cross-functional coordination with QA, analytics, and BI stakeholders.

• Migrated ETL pipelines from Informatica PowerCenter to Informatica Intelligent Cloud Services (IICS), handling diverse input and output data types across source and target systems to modernize the data integration layer.
• Developed end-to-end ETL mappings, built solution design documentation in Confluence, and used Oracle SQL for data extraction and validation — ensuring data accuracy at source.
• Executed an on-premises Oracle database migration to Oracle Cloud Infrastructure (OCI), handling end-to-end data integration and ensuring consistency between source and target systems throughout the migration lifecycle.
• Built and validated ETL pipelines in IICS end-to-end — covering data cleaning via Oracle SQL, unit testing, sanity testing, and system integration testing (SIT).
• Maintained version control and task tracking using GitHub and Jira across both projects within an Agile delivery model.

Bachelor of Technology (B.Tech) in Computer Science and Engineering

• Relevant courses: Data Structures & Algorithms, Operating Systems, Database Management Systems
• GPA: 8.90