Full Stack Engineer

We are seeking a highly skilled Lead Full Stack Engineer to join a high-impact engineering team building a next-generation Operational Data Hub.

This platform acts as a central “book of record” for enterprise data, enabling seamless data ingestion, processing, and distribution to downstream systems through real-time streaming and API-driven architecture. The team is driving a critical transformation from batch-based processing to real-time data streaming, supporting scalable and event-driven financial systems.

As a Lead Engineer, you will play a key role in designing, developing, and modernizing distributed systems, while remaining hands‑on and contributing directly to code.

What You’ll Do

Full Stack Development

• Design, build, and maintain scalable applications across the full stack
• Develop robust backend services and APIs using Java and Spring Boot
• Contribute to frontend and integration layers as needed

Real-Time Data & Streaming

• Build and enhance real-time data pipelines and ingestion frameworks
• Work with streaming platforms such as Kafka, Pub/Sub, Pulsar, Kinesis, or similar
• Help drive the transition from batch processing to event-driven architecture

Architecture & Technical Leadership

• Lead the design of distributed, high-performance systems
• Establish and promote engineering standards and best practices
• Ensure scalability, reliability, and security of production systems
• Mentor engineers and support technical growth across the team
• Collaborate with product, data, and engineering stakeholders
• Contribute to technical strategy and modernization initiatives

What You Bring

✅ Required Qualifications

• Strong experience in Java development, with deep expertise in Spring Boot
• Proven experience building APIs and backend services
• Hands‑on experience with streaming technologies (Kafka, Pub/Sub, Pulsar, Kinesis, Event Hubs, etc.)
• Experience designing and building distributed systems and data pipelines
• Solid understanding of event-driven architecture and real-time processing
• Demonstrated ability to lead technical initiatives and mentor engineers

Nice to Have

• Experience with Python in data or streaming environments
• Familiarity with stream processing frameworks (Flink, Spark Streaming)
• Exposure to AI/ML use cases or integrating AI capabilities into applications
• Experience with cloud-based data platforms