Mercedes-Benz Research & Development
|Principal Engineer, Distributed Systems & Platform Engineering
Bengaluru, Karnataka, India
Summary
Led distributed systems and platform engineering, driving large-scale data processing, API development, and infrastructure automation across multi-cloud environments for Mercedes-Benz.
Highlights
Architected and implemented fault-tolerant vehicle data pipelines, processing over 1M events/sec, and packaged reusable Flink frameworks adopted by 20+ global engineering teams.
Designed and deployed resilient multi-cloud event-driven architectures across AWS and Azure, ensuring scalable and reliable vehicle data distribution for analytics and integration.
Developed high-throughput, low-latency APIs for vehicle data ingestion and consumer-facing data-serving layers, consistently handling over 100K requests/sec under peak load.
Engineered scalable container platforms using Kubernetes (EKS/AKS) for Java/Golang microservices and Kafka workloads, automating provisioning and reducing deployment time by 90%.
Designed and implemented distributed storage solutions (DynamoDB, RocksDB, S3/ADLS) supporting over 200K strongly consistent reads and writes at production scale for low-latency metadata access and durable event storage.
Spearheaded an end-to-end multi-cloud migration from Azure to AWS, standardizing infrastructure patterns and achieving a 25% reduction in infrastructure costs.
Implemented comprehensive end-to-end distributed tracing and log aggregation using OpenTelemetry and Datadog, cutting streaming pipeline debugging time by 80%.
Developed and managed CI/CD pipelines for containerized Java, Golang, Flink, and Kafka workloads across AWS/Azure, automating provisioning for 200+ topics/event streams and enabling self-service for 100+ engineering teams.
Developed AI-assisted diagnostic systems, integrating with Datadog and Azure Data Explorer, to reduce production investigation time by 40%.