Aman Gulwani

Senior Data Engineer with 4+ years of experience in building large-scale ETL pipelines, data warehouse solutions and data lake architectures on cloud infrastructure. Proficient in technologies like Kafka, AWS, Python, Pyspark and SQL. Strong experience in data modeling, data quality checks, performance tuning, metadata management, CI/CD for data pipelines, data governance and automating ETL orchestration.

Senior Data Engineer at Codevyasa (2024-12 – Present)

• Led a team of 4 Data Engineers in designing, developing, and optimizing data pipelines and ETL processes.
• Architected and implemented an automated data lake (Bronze–Silver–Gold layers) using AWS S3, Glue (PySpark), and Python improving ETL scalability and reliability, reducing manual intervention by 60%.
• Optimized data storage in Amazon S3 using advanced partitioning and lifecycle policies, and designed analytical data marts using Star and Snowflake schema data models on Amazon Athena, enabling scalable SQL analytics on large datasets with high availability, cost optimization and improved query performance.
• Implemented Spark optimization techniques such as caching, multithreading, and broadcast joins, resulting in a 20% decrease in processing time for handling a daily load of around 2 Million records.
• Integrated AWS Glue Data Catalog for metadata management, enhancing data discoverability, governance, and compliance across the organization ensuring 99.9% schema consistency and automated version tracking.
• Delivered business KPIs through QuickSight interactive dashboards and data visualizations with Quicksight to provide actionable insights to stakeholders, facilitating data-driven decision-making.
• Implemented incremental data loads into Apache Iceberg tables enabling 35% faster data retrieval and query optimization through best practices in data partitioning, indexing, and resource allocation.
• Automated orchestration, backups, and quality checks via AWS Step-Functions, AWS Lambda, EventBridge and python scripts, enhancing workflow efficiency by 40%.

Data Engineer at Capgemini Technologies (2022-06 – 2024-11)

• Engineered efficient Ab-initio graphs and plans using Ab-initio GDE, enabling seamless data migration to AWS cloud with incremental and full load strategies, reducing data transfer time by 40%.
• Enhanced data workflow using AWS Service Catalogue, AWS S3, AWS Athena and AWS CloudWatch. Improved data processing efficiency by 35%, saving over 50 hours per month in analysis time.
• Optimized workflows through automation using shell scripting and Ab-initio graphs, streamlining metadata comparison and documentation tasks, resulted in a 60% increase in team productivity and 35 hours saved monthly

Data Engineer at Capgemini Technologies (2022-01 – 2022-05)

• Designed data pipelines on AWS using S3, Glue, Athena, and Iceberg enabling full & incremental data processing.
• Optimized data storage and query performance by managing metadata, snapshots, and schema evolution.

Bachelor of Technology in Computer Science – GLA University (2018-07 – 2022-06)