About the Role
Our Client is looking for a Director or VP of Data Engineering to lead the organisation’s data platform strategy, machine learning capabilities, and AI-driven product innovation.
This is a transformational, hands-on leadership role spanning data infrastructure, applied ML/AI, and domain-specific analytics. The successful candidate will partner closely with executive and product leadership to define and execute a company-wide AI strategy, embed intelligence across the platform, and position the organisation as a market-leading, AI-powered intelligence provider within its sector.
The role will oversee a multidisciplinary team of data engineers, software engineers, ML engineers, and data scientists, cultivating a culture of technical excellence, experimentation, and measurable impact aligned with the company’s mission.
Key Responsibilities
Data Platform & Infrastructure
- Own and evolve the organisation’s modern data platform, including warehousing, pipelines, governance, quality, and observability frameworks.
- Ensure data reliability, scalability, and trust across all products and internal functions.
- Establish standards for data contracts, lineage, and cataloguing.
- Evaluate and implement modern data stack technologies to optimise cost, performance, and developer experience.
- Operate hands-on when required, coaching engineers and leveraging AI-driven engineering practices to improve productivity and quality.
Machine Learning & AI
- Define and deliver the ML/AI roadmap in partnership with Product and Engineering leadership, embedding intelligence across the platform.
- Lead development of production-grade ML systems (e.g., anomaly detection, predictive modelling, automated data extraction, intelligent benchmarking).
- Drive responsible adoption of LLMs and generative AI to enhance reporting, insights, and user experience.
- Establish robust MLOps practices, including monitoring, experimentation, feature management, and CI/CD for models.
Domain-Specific Data & Analytics
- Build and scale differentiated data models and analytics frameworks aligned to industry standards and regulatory requirements.
- Translate complex regulatory and industry frameworks into scalable, productised data solutions.
- Enable advanced portfolio-level analytics and scenario modelling to support strategic decision-making.
Leadership & Culture
- Lead, mentor, and scale a high-performing, multidisciplinary Data & AI team.
- Foster a culture of technical excellence, accountability, experimentation, and continuous improvement.
- Partner cross-functionally to ensure alignment across Product, Engineering, and commercial teams.
- Attract, develop, and retain top-tier talent in a competitive market.
Strategy & Business Management
- Define and execute departmental strategy and OKRs aligned with the company’s broader data and AI vision.
- Translate business priorities into actionable technical roadmaps.
- Assess build-vs-buy decisions, manage tooling investments, and oversee vendor relationships and budgets.
Requirements
- 10+ years of hands-on experience in data engineering, machine learning, or applied AI, including 3-5+ years in a leadership capacity managing teams of 5 or more (15+ years combined hands-on and leadership experience for VP-level candidates).
- Educational background — formal or equivalent practical experience — in computer science, data science, engineering, or a related quantitative discipline.
- Demonstrated success delivering ML/AI-powered product capabilities at scale within a SaaS or data platform environment.
- Proven ability to lead and scale multidisciplinary teams across data engineering, machine learning, and analytics functions.
- Experience managing complex, cross-functional data and AI initiatives involving multiple stakeholders.
- Prior exposure to commercial real estate, sustainability/ESG, climate technology, or other regulated data environments is advantageous.
- Familiarity with industry standards and regulatory frameworks (e.g., GRESB, GHG Protocol, ENERGY STAR, CSRD, TCFD) is beneficial.
- A combination of professional and/or academic experience that demonstrates the competencies outlined above.
