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Davy

Wealth management and financial planning services

Head of Data and AI Engineering

Engineering LeadershipUpdated on Nov 11
Full-timeDublin, IrelandNo salary listed

About the Job

Requirements

  • A degree in a numerate or technical discipline, or equivalent senior industry experience.
  • A strong track record in data engineering, AI engineering, or data platforms, including at least five years leading technical teams that deliver data and AI solutions in production.
  • Deep enterprise data and AI platform experience, ideally with Snowflake, including architecture, security, performance, observability, resilience, cost, and service management.
  • Proven experience defining data and AI architecture, roadmaps, standards, and design governance.
  • Practical experience taking machine learning or generative AI solutions from proof of concept into production, including evaluation, monitoring, and lifecycle management.
  • Hands-on technical credibility in modern data engineering, such as SQL and Python, cloud platforms including Azure or AWS, orchestration, continuous integration and continuous delivery, and infrastructure as code, with the ability to review designs, guide engineers, and challenge technology choices and vendors objectively.
  • Experience building multidisciplinary engineering teams, modernizing ways of working, coaching others, and retaining scarce technical talent.
  • Experience in financial services or another highly regulated environment, with a working understanding of Responsible AI, GDPR, DORA, and the EU AI Act.
  • Strong stakeholder, commercial, and vendor-management skills, with the ability to explain technical choices in plain business language.

Responsibilities

  • Own and evolve the enterprise data platform, leading the strategy, architecture, roadmap, performance, resilience, and cost of the Snowflake-based platform and the engineering services supporting data and analytics products and AI solutions.
  • Set data and AI engineering architecture as the design authority by defining future-state architecture, standards, and reusable patterns; reviewing solution designs; setting the engineering quality bar; deciding what to build natively in Snowflake and where complementary technology is needed; and representing Data, Analytics, and AI at the Architecture Review Board.
  • Establish, with the AI Centre of Excellence, a controlled and repeatable route to production for generative AI, retrieval, unstructured data, and agent-based solutions, supported by MLOps and LLMOps practices for evaluation, monitoring, and controlled change.
  • Lead delivery of resilient pipelines and governed, trusted AI-ready data products with clear ownership, quality, metadata, semantics, and lineage for Finance, Risk, Operations, Investment Management, Analytics, and the Wealth Management front office, including regulatory reporting flows aligned to BCBS 239.
  • Embed Responsible AI, privacy, security, model-risk, and operational-resilience requirements into designs, with traceability, auditability, and human oversight, in partnership with Risk, Compliance, and Data Protection.
  • Establish support, observability, incident, recovery, and release arrangements for business-critical data and AI services, with transparent FinOps for platform and AI consumption costs.
  • Build and lead a blended team of data and AI engineers, establishing clear standards, career paths, and an appropriate mix of permanent, partner, and specialist capability, and embedding DevOps, DataOps, and AI-assisted engineering practices.
  • Act as the trusted data and AI engineering partner to senior stakeholders, explaining trade-offs clearly, and manage Snowflake, AI providers, and delivery partners, including DORA third-party risk requirements.
  • Agree a target architecture, roadmap, and operating model for Data and AI Engineering.
  • Define clear ownership across Data and AI Engineering, the AI Centre of Excellence, Technology, and the control functions.
  • Implement a controlled, repeatable production pathway for prioritized AI use cases.
  • Introduce reusable patterns for model access, retrieval, evaluation, monitoring, and AI integration.
  • Strengthen platform reliability, recovery, supportability, and cost transparency.
  • Improve the quality, metadata, lineage, and ownership of priority data products.
  • Establish a workforce, sourcing, and capability plan for the team.
  • Develop a prioritized roadmap to modernize or retire legacy data components.

Desired Qualifications

  • Experience with retrieval-augmented generation, vector search, document processing, or agent-based solutions is a strong advantage.

About the Company

Davy

Wealth management and financial planning services

Davy provides wealth and investment services in Ireland and the UK, offering financial planning and investment advice for high-net-worth individuals, SMEs, and institutional clients. It uses a team-based advisory approach to assess goals, craft tailored plans, and provide ongoing guidance through a network of professionals, backed by Bank of Ireland. The company differentiates itself with a long history (since 1926), strong client relationships, and a culture focused on client success, collaboration, and staff development, supported by its parent group for scale and stability. Its goal is to help clients achieve their financial objectives over time while growing its people and relationships.

Company size
501-1,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Dublin, Ireland
Founded
1929

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