Lead Data Platform Engineer; AI and Data Platforms

Job Title:Lead Data Platform Engineer (AI and Data Platforms)
Location:Belfast, Northern Ireland
Work Arrangement:Hybrid In-Office

We are seeking a Lead Data Platform Engineer to design and deliver the data and AI platform capabilities that accelerate safe, effective AI adoption across Biller Genie. This role sits at the intersection of data engineering, platform engineering, and applied AI. You will build the foundations that enable LLM powered and ML powered experiences in our Saa Splatform, andenable internal teams to use AI responsibly for decision making, support, operations, and engineering productivity.

You will own end-to-end platform outcomes across ingestion, storage, modelling, governance, and consumption. A core emphasis is building secure, governed access patterns for AI systems, includingretrievalaugmented generation (RAG), feature and embedding pipelines, evaluation harnesses, and agentic workflows. You will also shape how we expose data to AI tools using modern access patterns such as MCP servers, with strong controls for identity,authorisation, auditability, and data minimisation.

You will partner closely with Engineering, Product, Security, and business stakeholders to ensure our data is trustworthy, discoverable, secure, and AI ready, whileremainingpragmatic about delivery in a fast-paced fintech environment.

About Us:Biller Genie is an award-winning B2B SaaS platform focused on automating accounts receivable for SMBs. We are scaling our Belfast team rapidly and building the next generation of platform capabilities to support growth, including AI enabled product experiences and internal operating leverage.

• Define and deliver the technical roadmap for our AI and data platform, aligned to business outcomes, product strategy, and measurable AI adoption goals.
• Design and implement AI ready data foundations, including high qualitycanonicaldatasets, consistent entity resolution, and reliable event and telemetry capture to support analytics and AI features.
• Build andoperateingestion and transformation pipelines that support both batch and near real-time use cases, with strong observability, cost awareness, and reliability practices.
• Establish patterns for AI consumption of data, including:
• RAG oriented access (document and knowledge retrieval)
• Embeddings generation, storage, and lifecycle management
• Feature engineering pipelines where appropriate
• Evaluation datasets and feedback loops
• Define secure access points to governed data for AI systems, including the use of MCP servers where appropriate, with strong authentication,authorisation, rate limiting, and auditing.
• Own data governance for AI use, including lineage, cataloguing, data contracts, quality controls, retention, and controls for model inputs and outputs.
• Lead data compliance design and delivery for North American requirements, working with Security and Legal on policies, controls, auditability, and evidence generation.
• Implement and enforce security controls for AI and data systems, including least privilege access, encryption, key management, environment separation, and secure handling of sensitive data in prompts, context windows, and logs.
• Define andmonitordata quality and platform SLAs, including detection, triage, and remediation of data and AI related incidents.
• Partner with product and engineering teams to ship AI enabled SaaS capabilities, translating ambiguous problems into robust platform primitives and delivery milestones.
• Mentor engineers and drive platform standards across teams, including documentation, runbooks, and disciplined change management.

• Strong experience building andoperatingmodern data platforms in production environments.
• Demonstrable experience designing data access patterns for multiple consumers, including APIs and governed self-serve, with an emphasis on security and auditability.
• Strong understanding of AI enablement patterns on data platforms, such as RAG,embeddings pipelines, evaluation and monitoring, and the practical constraints of LLM systems.
• Proven ability to implement data governance and stewardship, including catalogues, lineage, quality, andstandardiseddefinitions, and to apply these in AI contexts.
• Demonstrable experience implementing security controls for sensitive data, including least privilege access, secure networking, encryption, and safe logging practices.
• Familiarity with North American data compliance expectations and the practical controls needed to meet them, including audit readiness and evidence generation.
• Strong SQL skills and experience with data modelling for analytics and operational use cases.
• Strong written and verbal communication, with the ability to influence across Engineering, Product, and Security.
• Pragmatic problem solving mindset, with a bias for outcomes and measurable impact.
• Legal right to work in the UK.

• Experience in fintech, payments, or other regulated environments.
• Experience with agentic systems and tool calling patterns,…