Associate Staff Engineer (Data Engineer -Apache Kafka, Flink, Java)

Job Description Requirements - Minimum 4+ years of experience in Data Engineering with a focus on real-time data processing and streaming technologies. - Strong hands-on experience in Java development and building enterprise-grade applications. - Expertise in Apache Kafka, including Producers, Consumers, Topics, Partitions, Consumer Groups, Kafka Connect, and event-driven architectures. - Hands-on experience with Apache Flink for real-time stream processing, stateful computations, windowing, and fault-tolerant data pipelines. - Experience designing, developing, and deploying scalable real-time streaming data pipelines. - Solid understanding of distributed systems, messaging patterns, and high-throughput, low-latency data processing. - Experience working with REST APIs, microservices, and integration frameworks. - Good understanding of data ingestion, transformation, and processing techniques in streaming environments. - Familiarity with real-time analytics and event-driven architectures. - Experience working in Banking, Financial Services, or other mission-critical environments is preferred. - Good understanding of containerization and cloud platforms is an added advantage. - Strong analytical, problem-solving, and debugging skills. - Excellent communication and stakeholder management skills. Responsibilities - Design, develop, and maintain scalable real-time data pipelines using Apache Kafka, Java, and Apache Flink. - Build and optimize low-latency, high-throughput streaming solutions for business-critical data processing needs. - Develop event-driven applications and data workflows to support real-time analytics and operational reporting. - Collaborate with architects, platform teams, and business stakeholders to understand data requirements and implement effective solutions. - Monitor, troubleshoot, and enhance streaming applications to ensure reliability, scalability, and performance. - Implement best practices for data quality, security, governance, and operational excellence. - Optimize Kafka and Flink applications for performance, resilience, and fault tolerance. - Participate in code reviews, design discussions, and technical solutioning activities. - Support production deployments and resolve issues related to streaming data platforms. - Contribute to continuous improvement initiatives by evaluating and adopting modern real-time data engineering practices and technologies.