Data Engineer

About Whitespace

Whitespace builds sovereign AI solutions for highly regulated and complex sectors. Our core product, Collective, is an AI operating system designed for secure, air-gapped, and sensitive environments. We are delivering at pace across high-profile government and commercial programmes and growing rapidly as a Series A-funded company.

The Role

We are looking for a Data Engineer to join our development teams as we build to enable decision superiority. You will work in a delivery-driven, highly-autonomous, full-stack engineering team, taking a proven data platform from prototype to a production system that ingests real-world data at scale and feeds advanced AI capabilities.

What You Will Do

• Design and harden scalable data pipelines, taking them from prototype to high-throughput production.
• Onboard diverse real-world data sources, normalising heterogeneous feeds into consistent, queryable schemas.
• Build and maintain the data foundations that enable AI scientists to integrate models and agents.
• Design and implement a knowledge layer that holds refined, resolved outputs of the pipeline.
• Enable AI agents to read from and write to that layer with full, auditable traceability back to source data.
• Implement secure, high-performance production code (Python) compliant with regulated environment standards.
• Contribute to the technical development and mentorship of the wider engineering team.

Essential Requirements

• Strong experience building distributed data pipelines at production scale, with a real understanding of where they break.
• Deep proficiency in Python and solid software engineering fundamentals (testing, version control, code review).
• Genuine grasp of data modelling, schema design, and data lineage/provenance.
• SC Clearance held or willingness to undergo.

Strong to Have

• Experience with streaming systems and knowing when they are appropriate (e.g., Kafka, Flink).
• Strong knowledge of large-scale data processing and open table/lakehouse technologies (e.g., Spark, Iceberg, Trino, object storage)
• Familiarity with knowledge graphs or graph databases.
• Experience integrating data platforms with ML workflows.
• Comfort with ambiguity and greenfield technical decisions in a scaling organisation.
• UK-based with willingness for occasional travel to client.

Desirable

• Clearance or eligibility.
• Entity resolution, record linkage, or data fusion across heterogeneous sources.
• Geospatial and/or time-series data at scale.
• Experience with containerised deployments (Docker, Kubernetes) in restricted/air-gapped networks.
• Interest in the traceability and trustworthiness of AI/agent outputs.

What We Offer

We believe in working hard and playing hard. When we are on, we are fully on, delivering for customers who depend on us. But we also believe that sustainable high performance requires genuine rest and recovery. That is why we have built one of the most generous working patterns in the industry:

• Four-day working weeks in July, August and December. Every Friday off, all month.
• Last Friday of every month off. January through June and September through November, the last Friday is a company day off.
• Early finish every remaining Friday at 3pm. On Fridays where you are working, you finish at 3pm.

In total, this adds up to approximately 22 additional full days off per year, plus the equivalent of a further 8 days in early finishes. That is roughly 30 days of additional time back on top of your standard annual leave. We trust our people to deliver and in return, we give them the space to recharge properly.

• Competitive salary and benefits package including company pension.
• Opportunity for equity options in a rapidly growing scale-up.
• Access to cutting-edge AI tools and technologies as core working instruments, not side projects.
• A genuine opportunity to shape how a high-growth company operates from the inside.
• The chance to work on technology trusted by the UK's most critical customers.
• A culture that values delivery over process, impact over optics and people over politics.