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WiseSenior Data Engineer II - Real Time Data Engineering
Wise seeks a Senior Data Engineer II - Real Time Data Engineering to join the Treasury Global Liquidity team in London on an on-site basis. In…
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About This Role
Wise seeks a Senior Data Engineer II - Real Time Data Engineering to join the Treasury Global Liquidity team in London on an on-site basis. In this full-time role, the successful candidate will help design and build the foundations of a real-time liquidity observability stack and core streaming data pipelines that drive global financial operations. This position suits an engineer who thrives in autonomous, cross-functional squads and wants to play…
Job Description
Wise seeks a Senior Data Engineer II - Real Time Data Engineering to join the Treasury Global Liquidity team in London on an on-site basis. In this full-time role, the successful candidate will help design and build the foundations of a real-time liquidity observability stack and core streaming data pipelines that drive global financial operations. This position suits an engineer who thrives in autonomous, cross-functional squads and wants to play a founding role in a high-visibility team.
The person in this position will pioneer the real-time Liquidity Attribution Model and architect high-throughput Kafka and Flink streaming pipelines that continuously process global bank transactions. Responsibilities include balancing demand against supply intraday, routing high-value transactions instantaneously, fueling machine learning forecasting models, and protecting the event architecture against out-of-order spikes, schema shifts, and network partitions. The role also involves mastering a Medallion architecture and pairing streaming pipelines with sub-second OLAP querying to give Treasury Ops instant visibility.
Wise operates cross-functional teams focused on collaborative problem-solving, utilizing RFCs, whiteboard sessions, and stakeholder loops to achieve technical clarity before production. The company builds financial products without borders and values diversity, equity, and inclusion across its international workforce.
Responsibilities
- Design and build the foundations of a real-time liquidity observability stack and core streaming data pipelines
- Pioneer the real-time Liquidity Attribution Model
- Architect high-throughput Kafka and Flink streaming pipelines to process global bank transactions continuously
- Balance demand against supply intraday and route high-value transactions instantaneously with fine-grained event controls
- Feed machine learning forecasting models to maximize yield on surplus capital
- Protect the event architecture against network partitions, out-of-order spikes, and schema shifts
- Pair streaming pipelines with sub-second OLAP querying to provide Treasury Ops with precise liquidity visibility
Requirements
- Build, deploy, and operate production-grade, low-latency stream processing pipelines using Apache Kafka, Kafka Streams, Flink, or Spark Streaming
- Manage duplicate events, late-arriving data, out-of-order log processing, and exactly-once processing semantics
- Apply windowing techniques, stateful stream processing, and high-throughput real-time aggregation across streaming pipelines
- Model data for real-time stream consumer patterns and downstream analytical stores including schema evolution with Protobuf or Avro
- Design defensively for dead-letter queues, backpressure management, circuit breakers, and state-store recovery
Core Skills
Benefits
- Salary between £111,000 and £145,000
- Restricted Stock Units (RSUs)
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the location and remote status of this job?
This role is located in London and operates on-site.
What is the salary and benefits package for this position?
The salary ranges from £111,000 to £145,000, and the compensation package includes Restricted Stock Units (RSUs).
What type of employment is this?
This is a full-time employment opportunity.
What technical experience is required for the role?
Candidates must have experience building low-latency stream processing pipelines with Apache Kafka, Flink, or Spark Streaming, handling event reliability and idempotency, performing real-time data aggregation, and implementing defensive engineering practices.
Sample Interview Questions
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