• Design and implement real-time streaming data pipelines for high-volume event data. • Develop and operate distributed data processing systems using technologies such as: • Apache Flink • Apache Kafka • Apache Druid • Build scalable ingestion pipelines capable of handling millions of events per second. • Design low-latency analytical data stores for operational dashboards and real-time analytics. • Optimize data pipelines for performance, scalability, and fault tolerance. • Work with product and analytics teams to translate business needs into real-time data models. • Build and maintain data observability, monitoring, and reliability frameworks. • Implement schema evolution and data quality controls across streaming pipelines. • Contribute to data platform architecture decisions and infrastructure design. • Mentor junior engineers and promote best practices in data engineering and distributed systems.