Abhinav Bajpai

I’m a Data Engineer at Cognizant Technology Solutions, working on end-to-end ETL and data integration for high-volume datasets. I design scalable data pipelines using PySpark and SQL, with a strong focus on performance, reliability, and maintainability.

I optimize Spark workloads and SQL queries to reduce data processing time, and I build ingestion frameworks for efficient data movement and storage using Apache Iceberg and Oracle. In production, I resolve issues and implement monitoring strategies to improve pipeline stability and availability.

Before my full-time role, I completed a Data Engineering Internship at Cognizant, where I built ETL pipelines ingesting and transforming data from Azure Blob Storage. I also applied data warehousing principles to design structured datasets for efficient storage, reporting, and analytics, collaborating in Agile teams.

My projects reflect the same mindset: scalable distributed ETL pipelines with robust cleansing, transformation, and validation workflows, plus reliable migration automation (CSV to Salesforce using SQL and Informatica PowerCenter). I enjoy turning raw data into trustworthy, well-modeled datasets that enable effective reporting and analytics.