Taranjit Singh

Led end-to-end architecture for Jira Cloud Platform workflows and automation services, including service decomposition, Kafka contract versioning, data partitioning strategies, and horizontal scalability to support enterprise tenants. Optimized RESTful APIs and GraphQL for real-time automation triggers, improving P95 latency by 32% through query optimization and Redis caching. Built distributed event pipelines with Kafka across microservices for reliable eventual consistency. Migrated legacy monolith components to containerized microservices on AWS ECS and Kubernetes, boosting deployment velocity and service isolation. Designed and maintained real-time ETL/ELT pipelines in Python to transform, enrich, and aggregate automation events into analytics-ready datasets stored in PostgreSQL and AWS S3. Delivered full-stack features with React + Next.js + TypeScript for admin dashboards. Collaborated with product managers and enterprise clients to design extensible APIs and maintain SLA complia

Delivered real-time financial data APIs using Node.js and Python with PostgreSQL. Designed data ingestion pipelines using Kafka to process live market feeds and low-latency streaming updates. Implemented Redis caching to support high-traffic endpoints. Built interactive portfolio and market analytics dashboards using React and TypeScript. Developed backend data aggregation services to compute financial metrics and normalize third-party feeds. Migrated legacy monoliths to microservices deployed on AWS ECS and Kubernetes, improving scalability and reliability.

Developed batch ETL pipelines in Python and SQL to process large-scale TV viewership and consumer behavior datasets. Built scheduled data processing jobs using Linux cron to aggregate raw measurement data into structured reporting tables. Designed RESTful-like APIs to expose analytics data for enterprise dashboards. Built reporting dashboards for internal analysts and client-facing teams. Optimized PostgreSQL queries for large workloads, reducing execution time by 35%. Maintained data validation checks across ingestion and reporting pipelines.