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