Director of Data Engineering & AI

Director of Data Engineering: AI & Data Platforms

Location: London (Hybrid – 1–2 days per week onsite)

Salary: Up to £160,000 + equity & benefits

Reports to: CTO

Overview of the Role

This is a rare opportunity to shape and lead the evolution of a next-generation, AI-native data platform within a high-growth, well-funded scale-up.

As Director of Data Engineering, you will operate at the intersection of strategy and execution—partnering closely with the CTO to define the long-term architecture, build a high-performing team, and transition the organisation from a traditional data platform to an agentic, AI-driven ecosystem.

You will inherit a capable team, but more importantly, you will define its future: how it scales, what it builds, and where it leverages external innovation. This role combines hands-on technical leadership (circa 50%) with team and organisational leadership (circa 50%), making it ideal for a builder-leader who thrives on both architecture and people.

Key Responsibilities

Strategic Leadership & Platform Vision

Partner with the CTO to define and execute the AI and data platform strategy, including critical build vs buy decisions

Establish a clear approach to partner vs upskill, ensuring the team leverages external innovation while building core internal capability

Shape the long-term vision for an agentic, AI-native data ecosystem

AI-Driven Data Platform Development

Lead the design of a unified AI search layer, combining vector, keyword, and graph-based approaches (e.g. GraphRAG)

Architect and scale agent-based systems and human-in-the-loop workflows

Oversee the development of a knowledge graph and data enrichment pipelines to unlock proprietary data value

Drive AI/ML Ops maturity, including LLM deployment, RAG pipelines, and evaluation frameworks

Engineering & Architecture

Define scalable, cloud-native architectures across GCP (preferred) and AWS

Lead cross-cloud data orchestration, ensuring seamless data flow from ingestion to intelligence layers

Guide technical decisions on tooling, frameworks, and platform evolution

Team Leadership & Growth

Lead and develop a team of data and AI engineers, including senior and staff-level contributors

Build a culture of engineering excellence, accountability, and continuous improvement

Hire, mentor, and scale the team in line with business growth

Delivery & Impact

Ensure engineering effort is focused on high-value, domain-specific problems

Improve velocity through modern engineering practices and AI-assisted development

Balance innovation with pragmatism—avoiding unnecessary reinvention while maintaining competitive advantage

Key Requirements

Leadership & Experience

Proven experience as a Director or Senior Engineering Manager leading data/platform teams

Strong track record of scaling teams and mentoring engineers (typically 5+ years in leadership roles)

Comfortable operating in a hands-on leadership capacity

Data Platform & Cloud Expertise

Experience building and scaling high-volume data platforms

Strong knowledge of GCP (BigQuery preferred) and exposure to AWS

Expertise in data pipelines, distributed processing, and Python-based data services

AI & Emerging Technologies

Exposure to or strong interest in agentic systems, LLMs, and AI-driven architectures

Experience with AI search (vector databases, hybrid search, GraphRAG) is highly desirable

Familiarity with knowledge graphs, ontologies, or complex data modelling

Strategic & Commercial Thinking

Experience making build vs buy and technology investment decisions

Ability to evaluate and integrate third-party tools, platforms, and partnerships

Strong alignment with business outcomes, not just technical delivery

Additional Information

Working Pattern: Hybrid (1–2 days per week in London office)

Salary: Up to £160,000 + equity + benefits (private healthcare, pension)

Team Size: ~4–5 engineers currently, scaling further

Career Progression: Clear path towards VP Engineering / CTO

Environment:

Stable, well-funded scale-up (not early-stage chaos)

Highly unique, proprietary datasets

Strong investment in modern AI tooling and practices

Interview Process (3 Stages)

Introductory conversation with senior leadership

Technical/design interview (architecture-focused)

Final interview with executive stakeholders (in-person)

This role is ideal for a senior engineering leader who wants to build, shape, and scale not just maintain. If you’re motivated by cutting-edge AI, complex data challenges, and genuine strategic influence, this is an opportunity to make a lasting impact.