Data Engineer

Role: Data Engineer (Java, Spring boot, Kafka)

Location: Whippany NJ (Hybrid -3 days a week)

• Lead the architecture, design, development, and operational stewardship of real-time streaming and data platform capabilities, remaining hands-on in key technical decisions and implementation where appropriate.
• Write, review, and guide high-quality production code for critical platform components, setting the bar for engineering standards, maintainability, resilience, and performance.
• Lead and develop engineering teams, providing technical direction, coaching, and delivery leadership while creating a culture of accountability, ownership, and continuous improvement.
• Own the design and production operation of resilient, low-latency streaming solutions that process critical financial events with strong guarantees for correctness, recoverability, observability, scalability, and control.
• Run streaming platforms at scale, including responsibility for production health, capacity, reliability, incident response, recovery, and continuous service improvement.
• Partner with business, product, architecture, and control stakeholders to shape roadmaps, solve complex engineering problems, and align platform delivery with business and regulatory requirements.
• Drive platform modernization across streaming, event processing, and data movement, balancing hands-on technical contribution with leadership across delivery priorities and dependencies.
• Establish strong engineering disciplines across testing, code review, release management, observability, incident response, capacity planning, and production readiness for real-time workloads.

To be successful you should have experience with:

• Deep experience designing, building, and operating real-time data platforms, streaming architectures, and distributed systems in complex enterprise environments.
• At least 8 years of hands-on experience running real-time streaming solutions in production at scale, with accountability for reliability, performance, observability, recovery, and operational stability.
• Strong software engineering capability, with fluency in Java programming and a track record of writing production-grade code and leading technical implementation for business-critical systems.
• Strong technical credibility in streaming and event-driven systems, including topics such as partitioning, ordering, stateful processing, delivery semantics, replay, fault tolerance, and platform resilience.
• Proven experience leading engineering teams, mentoring developers, and driving delivery while maintaining high standards across design, coding, testing, and operational support.
• Experience working with modern streaming and data technologies such as Kafka, Pulsar, Kinesis, Flink, Spark Structured Streaming, or equivalent platforms, with the judgment to select the right patterns for the problem.
• Strong understanding of control, governance, and risk management expectations for production data platforms, particularly where resilience, auditability, and data correctness are business critical.

Some other highly valued skills may include:

• Experience leading engineering organizations using modern data platforms such as Databricks or equivalent ecosystems for large-scale data and analytics workloads.
• Working knowledge of cloud-native platform capabilities across compute, storage, orchestration, observability, and security in support of resilient data services.
• Strong understanding of software engineering lifecycle practices, including CI/CD, platform automation, release controls, and operational governance for critical services.
• Experience operating in financial services, payments, trading, or similarly regulated environments where non-functional requirements, controls, and recoverability are essential.
• Ability to communicate complex technical strategy clearly to senior business and technology audiences, building confidence, alignment, and support for change.