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

Job description

We're Nagarro. We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at a scale across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 36 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in! 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.