About IFS Logistics
IFS Logistics helps enterprise shippers connect transport data, manage carrier spend, and automate execution. The product began as 7bridges and is now part of IFS, with a small, focused team working closely with the commercial organisation.
We build SaaS products that customers can run themselves. The platform handles busy, data-heavy logistics operations, so reliability and clear operational visibility matter every day.
The role
You will join Diego Silva, our Lead DevOps Engineer, as the second DevOps specialist responsible for the platform. You will take ownership of substantial workstreams while partnering closely with application and data engineers.
There is useful work to do from day one. We want every merge request reviewed, production deployments controlled, and releases running to a predictable cadence. Those practices are not yet consistent across the team. Our monitoring also needs to cover more of the application and data estate, and our pipelines need stronger detection, recovery and ownership when something fails.
You will help turn those goals into working systems: better signals, safer delivery paths, faster recovery and infrastructure the team understands. This is a hands-on role for someone who enjoys improving a live platform rather than waiting for a perfect starting point.
What you'll work on
- Build useful observability. Improve metrics, logs, traces and alerts across the Django API, data pipelines and warehouse so the team spots problems early and can diagnose them quickly.
- Improve CI/CD. Make GitLab pipelines fast, dependable and difficult to bypass, with review and deployment controls built into the workflow.
- Reduce operational risk. Remove direct-to-production paths, improve release controls and help the team establish a reliable weekly release rhythm.
- Manage cloud cost. Make GCP spend visible by workload and product area, then reduce waste without compromising service quality.
- Develop infrastructure as code. Improve our Terraform and Kubernetes setup so environments are reproducible, secure and clearly documented.
- Strengthen data-pipeline reliability. Add failure detection, freshness checks, replay paths and clear ownership across Datastream, Pub/Sub, dbt and BigQuery.
- Respond and learn. Help shape incident management and on-call practices, then turn recurring failures into automated checks and safeguards.
What we're looking for
- Strong experience running production services on GCP.
- Practical knowledge of Kubernetes and Terraform, including operating and improving infrastructure rather than only deploying to it.
- Experience designing and maintaining CI/CD pipelines in GitLab or a similar platform.
- Evidence that you have instrumented a real system and improved its metrics, logging, tracing or alerting.
- Examples of treating cloud cost as an engineering concern: attributing spend, finding waste and making informed trade-offs.
- Experience operating data pipelines, such as streaming ingestion, warehouse loads, dbt transformations or similar workflows.
- The confidence to challenge an unsafe change and explain what needs to happen before it can ship.
- Practical experience with AI coding tools such as Cursor or Claude, including how you provide context and verify infrastructure changes before accepting them.
- The ability to assess an imperfect system, decide what matters most and carry the improvement through to production.
Nice to have
- Enough Python experience to investigate or fix issues in a Django application.
- Security or compliance work in an enterprise SaaS environment.
- Experience designing incident-management or on-call processes.
- BigQuery cost and performance tuning.
Tech stack
- Frontend: React, TypeScript, Node.js.
- Backend & APIs: Python, Django REST Framework.
- Data & analytics: BigQuery, dbt, Cube Cloud, Superset.
- Streaming & ingestion: Pub/Sub, Datastream.
- Cloud & infrastructure: GCP, Kubernetes, Terraform, GitLab CI/CD.
- AI: LLMs, RAG, agent frameworks, evals.
- Developer tooling: Cursor & Claude (for development), Notion (for specifications), Jira (for delivery).
What success looks like in the first six months
- Our monitoring identifies production problems before customers report them.
- Pipeline failures are detected quickly, assigned clearly and recoverable without improvised fixes.
- Merge-request review and controlled production deployment are enforced through the delivery pipeline.
- Weekly releases run predictably without creating unnecessary overhead for engineers.
- GCP spend is attributable to workloads and product areas, with clear action taken on the main cost drivers.
- Application and data teams have a responsive platform partner who improves their ability to ship safely.
Why we're hiring now
The team is growing and the platform needs to support a faster pace of delivery. That requires better visibility, stronger data pipelines and release controls that work without relying on individual memory or manual intervention.
Diego currently carries too much of that responsibility alone. This role adds the capacity to improve the platform while still supporting the product in production, and gives the team another engineer who can take a problem, form a plan and see it through.
This successful candidate will need to be based in the UK or the Netherlands. We're a remote team with clusters of people in London and Amsterdam.
We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.