Rajat Madaan — Data Engineer

Turning complex data ecosystems into scalable AI-ready platforms. With 12 + years of experience across Amazon, Expedia, and Deloitte, I build end-to-end data platforms that power analytics, experimentation, and machine learning at global scale. My core strength lies in transforming messy data into trusted, governed assets that feed AI and LLM systems reliably. 🔹 Architecture & Tech Stack – Python · SQL · Java · Scala | Spark · Databricks · Snowflake · dbt · Airflow · Kafka | AWS (S3, Glue, Redshift, Lambda) · GCP · Terraform · Kubernetes 🔹 Specialties – Data Modeling, Lakehouse Design, Data Governance, Feature Stores, Observability, Cost Optimization 🔹 AI Integration – LLM data pipelines, vector stores, feature management for ML and Generative AI workloads 💡 Recent impact: • Built a Spark-on-Databricks lakehouse processing > 10 PB daily, cutting ETL costs by 32 %. • Modernized Amazon asset data lake (21 + services) for real-time governance and analytics. • Implemented Airflow + dbt framework for A/B experimentation data, reducing latency from hours → minutes. I’m now focused on senior/principal-level roles where data engineering meets AI — helping teams build high-trust pipelines that feed LLM and analytics platforms with speed and accuracy. Let’s connect → to discuss AI Data Engineering / Platform roles or collaboration on LLM infra projects.

Stackforce AI infers this person is a Data Engineering expert specializing in scalable AI-ready platforms.

Location: Seattle, Washington, United States

Experience: 12 yrs 5 mos