Finance Data Engineer

Who we are

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. 

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. 

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. 

The role

This role sits at the intersection of finance automation and data sovereignty. As Finance Data Engineer, you will own the execution of Helsing's agentic finance build: designing and deploying automated pipelines, building AI-driven validation layers, and making the company's finance data flows self-correcting. You will work closely with the Principal SAP Finance & Data, who sets the architecture, while you own the implementation. A critical part of this mandate is data protection. You will define the classification and routing framework for all Finance AI workloads, ensure that no sensitive data reaches public LLMs or unapproved systems, and maintain the sovereignty controls that keep Helsing compliant with its government contract obligations.

The day-to-day

• Own the agentic finance build roadmap: identify, prioritise, build, test, and iterate on AI agents that replace manual finance workflows, using frameworks such as LangChain, CrewAI, or AutoGen, from prompt design through to production deployment

• Design and maintain automated data pipelines connecting SAP, Abacum, and Citi, and build AI-driven validation layers that govern data quality across the Finance stack

• Define and maintain the data classification and routing framework for all Finance AI workloads, specifying which data is classified or export-controlled, which models and environments are approved for each data tier, and ensuring all agents operate within those boundaries at all times

• Design and implement self-healing workflows in which agents detect their own errors, apply correction logic, and escalate only genuine exceptions to human review, and monitor pipelines using observability tooling such as LangSmith

• Partner with the ICS Lead and Legal to ensure every automated workflow is audit-compliant, leaves a full traceable record, and meets the data sovereignty requirements of Helsing's government contracts