Full Stack Engineer
Summary
Lead engineer building a real-time financial data hub using Java/Spring Boot and Kafka, migrating from batch to event-driven pipelines while mentoring the team.
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