Rupam Sau

About Rupam Sau

I\'m a Data Engineer with 4+ years of experience building large-scale data platforms on Azure and Databricks — from ingesting raw enterprise data to delivering clean, reliable datasets that power real business decisions. My work sits at the intersection of distributed computing, cloud infrastructure, and analytical engineering. I\'ve designed and operated both batch and real-time streaming pipelines handling terabytes of data, with a strong focus on performance, reliability, and scalability. Whether it\'s tuning a Spark job to cut execution time by 40%, architecting a Medallion Lakehouse from scratch, or ensuring exactly-once semantics in a high-throughput streaming system — I care deeply about building things that actually work in production. I\'m comfortable across the full Azure data stack — ADF, ADLS Gen2, Synapse, Event Hubs — and I live in Databricks and Delta Lake day to day. Python and PySpark are my primary tools, though I\'m equally at home writing complex SQL or picking up Scala when needed. Beyond the technical side, I\'ve closely collaborated with data scientists and BI engineers to ensure the data they receive is consistent, well-modeled, and timely — because good data engineering isn\'t just about pipelines, it\'s about trust. I hold Databricks Professional & Associate certifications, Azure DP-203, and a Databricks GenAI Engineer Associate cert — reflecting my commitment to staying current as the data landscape evolves. If you\'re looking for someone who can take on complex, enterprise-scale data challenges and turn them into robust, production-ready solutions, I\'d love to connect. Email: r••••••••@gmail.comPhone:+91••••••••34

Experience

1. Associate (Data Engineer)

   Cognizant

   Jul 2025 — Present · Kolkata, IN

   Development1. Driving the data engineering initiatives for a major insurance client, architecting cloud-native data solutions on Azure.2. Designing and developing scalable data pipelines on Azure Databricks, processing 10+ TB of data across multiple domains, including real-time IoT streaming using Kafka and Spark Structured Streaming.3. Building robust ETL/ELT frameworks with PySpark and Delta Lake that cut data processing time by 45% and significantly improve data quality.4. Working closely with Data Scientists, BI Engineers, and business teams to deliver analytics-ready datasets that support key decision-making.5. Implementing DataOps practices—CI/CD, automated testing, and IaC with Azure DevOps—resulting in 60% faster deployment cycles.Support1. Providing L3 production support for mission-critical data platforms, maintaining 99.5% uptime for pipelines supporting 200+ downstream systems.2. Leading high-priority incident resolution and root cause analysis, reducing MTTR from 4 hours to under 90 minutes.3. Setting up strong data quality and anomaly-detection frameworks using ADF and Databricks, preventing issues before they reach business users.4. Mentoring L1/L2 teams and creating detailed runbooks and knowledge-base documentation, improving first-call resolution by 35%.5. Driving performance tuning and cost-optimization efforts that reduce compute costs by 30% while ensuring all SLAs are met.

Education

• Future Institute Of Engineering and Management

  B.tech, Computer Science

  2018 — 2022