Job description
We are looking for a Senior Data Engineer with deep expertise in real-time data streaming and distributed data processing to design, build, and scale next-generation data platforms.
This role is critical in enabling event-driven architecture and real-time analytics for mission-critical banking systems, particularly across risk and compliance functions .
You will collaborate closely with data architects, platform engineers, and business stakeholders to deliver low-latency, high-throughput data pipelines that power advanced analytics and decision-making.
Key Responsibilities Design, develop, and maintain real-time streaming pipelines using Apache Kafka, PySpark, and Flink Build scalable and fault-tolerant event-driven data architectures Process high-volume streaming data with low latency and high reliability Integrate data from multiple sources into centralized data platforms (Data Lake / Lakehouse) Optimize data pipelines for performance, scalability, and cost efficiency Ensure data quality, governance, and compliance aligned with banking standards Work with cross-functional teams to translate business requirements into technical solutions Monitor and troubleshoot streaming jobs and production pipelines Required Skills & Experience 5+ years of experience in Data Engineering Strong hands-on experience with: PySpark / Spark Streaming Apache Kafka (Producers, Consumers, Kafka Streams) Apache Flink or other real-time processing frameworks Experience building real-time / near real-time data pipelines Strong understanding of distributed systems and event-driven architecture Proficiency in Python / Java / Scala Experience with data lakes, ETL/ELT pipelines, and big data ecosystems Knowledge of cloud platforms (AWS / Azure / GCP) is a plus Familiarity with banking, risk, or compliance data systems is highly preferred Preferred Qualifications Experience working in financial services or banking domain Exposure to data governance, regulatory reporting, or compliance systems Knowledge of CI/CD pipelines and DevOps practices for data platforms
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