• Own and operate event-driven data pipelines and backend systems built in Python, supporting business-critical enterprise integrations.

• Designed idempotent, retry-safe processing with explicit concurrency control to ensure production stability under parallel execution

• Built async APIs and queue-based background workers to support long-running workloads and high-volume data generation.

• Deployed cloud-native services using Docker, Kubernetes, and CI/CD pipelines across UAT and production environments.

• Developed scalable data pipelines and ETL/ELT workflows using Python and distributed processing frameworks.

• Built and maintained RESTful and asynchronous APIs to support real-time and batch integrations.

• Integrated event streaming and caching technologies to improve throughput and system responsiveness.

• Containerized and deployed applications using Docker in cloud environments with automated releases.

• Supported internal systems and user-facing issues with rapid turnaround time

• Assisted with server monitoring and maintaining reliability

• Partnered with technical teams to diagnose software and network issues