Rishabh D.

About Rishabh D.

I work with data at a scale where reliability matters more than theory.Over the last 3 years as a Data Engineer at Cognizant, I’ve helped teams trust their data by designing and maintaining PB-scale batch and streaming pipelines used by daily business consumers.My work lives at the intersection of Palantir Foundry and Snowflake — building pipelines in Pipeline Builder and Code Repos, modeling business entities through Ontology, and delivering analytics via Contour, Workshop, and Quiver. On the Snowflake side, I’ve worked deeply with Streams, Tasks, Snowpipe, Dynamic Tables, and performance tuning to keep systems fast and cost-efficient.One of the most critical challenges I’ve handled was migrating large Snowflake tables to Iceberg — involving petabytes of data — without disrupting live ingestion or downstream consumers. The result was improved scalability, reduced operational risk, and better long-term cost control.Along the way, I’ve focused on compute cost optimization, incident reduction, and automation — because stable data platforms quietly enable better decisions.I enjoy solving complex data problems, simplifying systems that have grown too large, and building pipelines that teams can rely on every day.

Experience

1. Associate

   Cognizant

   Oct 2025 — Present

   Built and maintained PB-scale batch and streaming data pipelines on Palantir Foundry, supporting daily business consumers across analytics and operations. • Designed end-to-end pipelines using Pipeline Builder and Code Repos, ensuring reliable ingestion, transformation, and delivery of large datasets. • Modeled complex business data using Palantir Ontology, improving data discoverability and usability for analytics teams. • Delivered insights through Contour, Workshop, and Quiver, enabling stakeholders to consume data without engineering dependencies. • Migrated large Snowflake tables to Iceberg format involving petabytes of data, completing the migration without impacting live ingestion or downstream workflows. • Implemented Snowflake Streams, Tasks, Snowpipe, and Dynamic Tables to support near-real-time and scheduled data processing. • Performed compute cost optimization in Snowflake and Foundry, reducing unnecessary compute usage while maintaining performance. • Reduced production incidents by improving pipeline monitoring, automation, and recovery workflows. • Worked across GCP and Azure environments, supporting cloud-native data processing and storage patterns.

Education

• Lakshmi Narain College of Technology, Kalchuri Nagar, Raisen Road, Post Klua, Bhopal-462021

  Bachelor of Technology - BTech, Mechanical Engineering

  2018 — 2022