Senior Data Engineer II - Real Time Data Engineering
Summary
Senior Data Engineer at Wise (posted via D8 LooP) designing and operating real-time streaming data pipelines and a liquidity observability stack for global treasury operations. Day-to-day work centers on Apache Kafka/Kafka Streams, Flink and Spark Streaming, schema modeling with Protobuf/Avro, and low-latency OLAP queries.
Wise · api.smartrecruiters.com · checked today
Wise is a global technology company, building the best way to move and manage the world’s money. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money.
Skills, with evidence
Advanced experience in modeling data for both real-time stream consumer patterns and downstream analytical stores (e.g., event schemas, state store schemas, schema evolution with Protobuf/Avro). must have
You will instinctively design for dead-letter queues, backpressure management, circuit breakers, and state-store recovery. must have
- Idempotency & backfills 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. must have
- Streaming Proven track record building, deploying, and operating production-grade, low-latency stream processing pipelines using Apache Kafka (or Kafka Streams, Flink, Spark Streaming). must have
- Cost & performance Navigating high-stakes trade-offs across latency, financial cost, regulatory boundaries, and customer experience is second nature to us.
In this role, you will help design and build the foundations of our real-time liquidity observability stack and core streaming data pipelines that power global financial operations at Wise.
Streaming Data Modeling: Advanced experience in modeling data for both real-time stream consumer patterns and downstream analytical stores (e.g., event schemas, state store schemas, schema evolution with Protobuf/Avro).
- Spark Advanced Stream Processing & Event-Driven Architecture: Proven track record building, deploying, and operating production-grade, low-latency stream processing pipelines using Apache Kafka (or Kafka Streams, Flink, Spark Streaming).
- Warehousing You will also master a cutting-edge Medallion architecture and pairing streaming pipelines with sub-second OLAP querying, empowering Treasury Ops with instant, precise visibility into unencumbered liquidity redefining how global treasury operates at scale long before end-of-day.