Responsibilities

• Design, develop, and maintain real-time streaming use cases leveraging Azure Service Bus, Event Hubs, Kafka, and Azure Functions.
• Work with Spark Streaming (unbounded data, streaming data frames, checkpoints, streaming joins, etc.) to build scalable pipelines.
• Deploy and manage workloads on Kubernetes using containers and microservices-based architectures.
• Implement robust monitoring and observability with Prometheus, Grafana, and ELK stack.
• Work with modern data lake table formats such as Delta Lake, Apache Iceberg, and Apache Hudi.
• Set up and optimize analytical databases and query engines like Trino, StarRocks, and ClickHouse for high-performance data querying.

What We’re Looking For

• Proven hands-on experience with real-time streaming platforms and data integration.
• Strong background in distributed data systems and streaming frameworks.
• Expertise in Kubernetes, microservices, and containerized deployments.
• Solid knowledge of observability and monitoring stacks.
• Familiarity with modern data lakehouse technologies and query engines.
• A problem-solver who thrives in dynamic environments and enjoys working with emerging technologies.

Why Join Us?

• Opportunity to work with cutting-edge data platforms and real-time technologies.
• Collaborative, innovation-driven environment.
• Growth-focused role with exposure to large-scale data engineering challenges.