Data Engineer

About Ripjar

Ripjar was founded by veterans of GCHQ to bring national security-grade intelligence tools to the fight against financial crime. Financial crime funds human trafficking, terrorism, corruption and sanctions evasion on a global scale, and the organisations on the front line need technology built to match the threat.

Today, Ripjar's AI-native software and data fusion products are used by governments, the world's largest banks, and global enterprises to automate the detection, investigation and monitoring of serious financial crime. Every day, hundreds of customers and thousands of daily active users rely on the platform to screen hundreds of millions of names for risk in real time, prevent money laundering and stop terrorist financing.

If you want your work to matter, this is where it happens.

We are a remote first team, with a head office based in Cheltenham. This position is open to UK wide candidates. If you are based near Cheltenham, you are more than welcome to work from our office at any time.

About the Role

We see a Data Engineer as a software engineer who specialises in distributed data systems. You’ll join the Data Engineering team, whose prime responsibility is the development and operation of the Data Collection Hub, a platform that ingests data from many sources, processes/enriches it, and distributes it to multiple downstream systems.

We’re looking for someone with 2+ years of industry experience building and operating production software who enjoys working across data pipelines, distributed systems, and operational reliability.

What you’ll do

• Engineer distributed ingestion services that reliably pull data from diverse sources, handle messy real-world edge cases, and deliver clean, well-structured outputs to multiple downstream products.
• Build high-throughput processing components (batch and/or near-real-time) with a focus on performance, scalability, and predictable cost, using strong profiling and measurement practices.
• Design and evolve data contracts (schemas, validation rules, versioning, backward compatibility) so downstream teams can build with confidence.
• Own production quality: write maintainable code, strong unit/integration tests, and add the observability you need (metrics/logs/tracing) to diagnose issues quickly.
• Improve platform reliability by hardening pipelines against partial failures, retries, rate limits, data drift, and infrastructure issues—then codify those learnings into better tooling and guardrails.
• Contribute to CI/CD and developer experience: faster builds, better test signal, safer releases, and automated operational checks.