Headline

Data Engineer | Azure Databricks · ADF · PySpark · SQL | Building Scalable ETL Pipelines | 3.6 Yrs @ Cognizant | Open to Opportunities

About

I build data pipelines that actually scale — and then make them faster. As a Data Engineer at Cognizant, I've spent 3.6 years designing and optimizing ETL workflows that process 1M+ records daily using Azure Databricks, ADF, and PySpark. I don't just move data — I cut reporting latency by 30%, automated batch workflows to reduce execution time by 80%, and built a custom ingestion framework that saved $800/month in infrastructure costs. My stack: Azure Data Factory · Databricks · Apache Spark · PySpark · SQL · Power BI · Delta Lake · Python What sets me apart is ownership. I take ambiguous data problems, architect clean solutions, and measure the outcome. I've worked across data ingestion, transformation, quality validation, and BI reporting — giving me full-pipeline visibility that most engineers in my space don't have. I'm actively exploring Data Engineering / Analytics Engineering roles where I can contribute to data platform architecture and drive real business decisions through reliable data infrastructure. 📩 Open to conversations — DM me or reach out at abhishekchavan1798@gmail.com

Location

Mumbai Metropolitan Region

AI stack

gemini

langchain

ollama

openai

Work experience

Data Engineer at Cognizant, Nov 2022 - Present · 3 yrs 8 mos, Pune District, - Architected end-to-end ETL pipelines on Azure Databricks + Apache Spark, processing 1M+ records daily with 99.9% SLA adherence - Slashed reporting latency by 30% by re-engineering data transformation logic, directly improving time-to-insight for downstream analytics teams - Engineered a custom data ingestion framework that reduced dependency on Azure Data Factory, cutting infrastructure costs by $800/month (~$9,600/year) - Automated batch processing workflows end-to-end, improving operational efficiency by 40% and cutting execution time by 80% - Drove data quality across pipelines through rigorous SQL and PySpark-based cleansing, transformation, and validation — reducing downstream reporting errors - Optimized Spark jobs and SQL queries for compute efficiency, reducing cluster runtime and cutting cloud resource costs - Delivered 5+ Power BI dashboards for business intelligence teams, enabling self-serve analytics for non-technical stakeholders - Maintained SLA-based data refresh processes across production pipelines, ensuring zero downtime for critical business reporting

Education

Master of Computer Applications - MCA, Information Technology, MIT Arts, Commerce & Science College ( MITACSC )

Bachelor of Science - BS, University of Mumbai