Staff Software Engineer

Acts as a project or system leader, coordinating the activities of other engineers on the project or within the system Determines the technical tasks that other engineers will follow Actions result in the achievement of customer, operational, program or service objectives Proactively improves existing structures & processes Exercises judgement in reconciling diverse and competing priorities (time, quality, complexity risk) to identify optimal solutions to problems Notices patterns and condenses repetition into densely meaningful generalized solutions Collaborates with management to set/improve standards for engineering rigor Lead the architecture and development of a distributed streaming and real-time compute platform Design and evolve core services for ingestion, processing, state management, and serving Drive low-latency, high-throughput system design with strong SLO and reliability guarantees Own complex, cross-team technical initiatives spanning compute, storage, and serving layers Design and implement high-performance backend services using Java and JVM technologies Define platform APIs, SDKs, and abstractions to enable product and ML teams Establish engineering best practices for streaming systems (backpressure, fault tolerance, exactly/at-least-once semantics) Review designs and code across teams, providing architectural guidance and technical mentorship Partner closely with infrastructure, ML, and product teams to support real-time use cases Balance near-term delivery with long-term platform evolution and tech debt management 5+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Deep expertise in Java and the JVM ecosystem Strong experience with distributed systems and stream processing architectures Hands-on experience with real-time data processing frameworks (e.g., Flink, Spark Streaming, Kafka Streams, or similar) Solid understanding of streaming concepts: event time vs processing time, windowing, stateful processing, checkpoints Experience designing low-latency, high-throughput backend services Strong knowledge of messaging systems such as Kafka or Pulsar Experience with API design (REST, gRPC) Familiarity with data stores used in real-time systems (key-value, time-series, or NoSQL) Experience deploying and operating systems in cloud environments (GCP, AWS or Azure) Experience with feature stores, online serving, or real-time ML pipelines Knowledge of stream reliability patterns (replay, idempotency, deduplication) Experience with observability in distributed systems (metrics, tracing, alerting) Familiarity with Kubernetes and containerized deployments Experience building internal platforms used by multiple teams Background in risk, fraud, payments, or other latency-critical domains What Success Looks Like The streaming platform consistently meets latency and throughput SLOs at scale Product and ML teams can onboard new real-time use cases quickly and safely Platform abstractions reduce complexity for downstream teams System failures are rare, contained, and easy to diagnose The organization increasingly relies on the platform for mission-critical workflows