Requirements

Proficient with Java or Scala
Proven experience building, deploying, and operating production-grade, low-latency stream processing pipelines using Apache Kafka (or Kafka Streams, Flink, Spark Streaming)
Deep hands-on experience handling duplicate events, out-of-order log processing, late-arriving data, and exactly-once processing semantics in financial or mission-critical domains
Strong expertise in windowing techniques (tumbling, sliding, session windows), stateful stream processing, and high-throughput real-time aggregation across streaming data pipelines
Advanced experience in streaming data modeling for both real-time stream consumer patterns and downstream analytical stores (e.g., event schemas, state store schemas, schema evolution with Protobuf/Avro)
Instinctive defensive engineering skills including design for dead-letter queues, backpressure management, circuit breakers, and state-store recovery

What you'll be doing

Design and build the real-time liquidity observability stack and core streaming data pipelines that power global financial operations
Architect high-throughput Kafka and Flink streaming pipelines processing global bank transactions
Balance demand against supply intraday with low-latency streaming pipelines delivering instant, global liquidity visibility
Route high-value transactions instantaneously with fine-grained event controls
Fuel ML forecasting models to maximize yield on surplus capital
Bulletproof event architecture against out-of-order spikes, network partitions, and schema shifts
Master a Medallion architecture pairing streaming pipelines with sub-second OLAP querying
Collaborate closely with Product Managers, Data Scientists, Quantitative Analysts, and Treasury Ops in a highly autonomous, cross-functional team
Engage in clear, actionable communication and thorough design reviews to raise engineering standards
Navigate trade-offs across latency, cost, regulatory boundaries, and customer experience using RFCs, whiteboard sessions, and stakeholder feedback