Summary
Dipankar Niranjan is a software engineer with 8 years of experience building reliable, cloud-native distributed systems and ML-driven observability at scale. Having worked across finance, research, and enterprise SaaS, he’s driven database observability and capacity management at Salesforce and now focuses on fault tolerance infrastructure at Meta. His background blends deep systems expertise (Kubernetes, Java, AWS) with applied ML and data engineering (Python, Kafka, Airflow, Iceberg), and he has a history of accelerating developer tooling—from optimizing pandas/NumPy for quant workflows to prototyping invoice ingestion using OCR. Academically rooted in CS research on brain connectivity and social computing from IIIT Hyderabad and Columbia, he brings a results-oriented, data-informed approach to complex production problems. Notably, he has translated research-grade analysis into production impact, such as designs that saved large manual workloads and improved observability for large-scale DB deployments.
8 years of coding experience