Data Engineer

Design, develop, and maintain distributed data pipelines on AWS that ingest, process, and deliver data at scale. Implement both batch and event-driven data processing patterns using AWS-native services.

Build and support event-driven data solutions using asynchronous, decoupled architectures to enable near real-time processing and system scalability.

Provide hands-on engineering using Python and AWS Glue with pyspark to process large datasets efficiently. Apply best practices in distributed systems, performance optimization, and fault tolerance.

Ensure end-to-end data quality, implement validation and monitoring checks, establish data lineage, and manage orchestration workflows to ensure reliable and auditable data movement.

Design and develop RESTful and event-driven APIs to expose data and data services to internal and external consumers.

Build and deploy data services using Docker containers and manage workloads on Kubernetes following cloud-native and DevOps best practices.

Collaborate with data consumers, architects, platform teams, and business stakeholders to gather requirements, design scalable solutions, and deliver high-quality outcomes.

• Contribute to monitoring, logging, alerting, and incident response for data platforms. Ensure systems meet reliability, performance, and security standards.