Head of Technology, Data & Knowledge
Role details
Job location
Tech stack
Job description
You will be working inside an AIM-listed Venture Capital firm, working directly with the executive leadership and a high-performing investment team. You will help create a dynamic and entrepreneurial story in the financial services/VC industry and shape how technology, data and AI are used to drive investment decisions and scale the business. You will take ownership of a high-potential technology and data environment, combining traditional VC investment practices and structured data with practical AI tools to create an insight-driven platform and a real competitive edge. The role focuses on consolidating the existing technology stack, databases and company know-how into practical and scalable systems, retaining the knowledge of the organization, increasing the capacity of operations, and providing decision-grade intelligence. A key focus will be the pragmatic and selective implementation of AI tools across the practice, building on existing systems and initiatives. This is a commercially engaged role, requiring someone who is hands-on and delivery-oriented; success will be measured by speed of adoption, efficiency gains, improved quality of investment insight enabling growth. This is a builder role you won't inherit a polished system - you'll shape it., Own and continuously improve EMV's core technology stack (including Salesforce, MS365, Box, Slack, Asana, Delio), evaluating and adopting emerging tools. Integrate workflows and technology stack with external data sources, ensuring they are well-integrated, secure, reliable, and usable. Manage SaaS vendors, external developers, and specialist technology partners.
Data, Reporting & Commercial Insight
Design and implement a proprietary practical data strategy, including improved systems and storage. Build dashboards, reports and alerts that drive action, not just visibility. Work proactively with the investment team to identify information that actually drives and enables decisions.
Knowledge Management & Structured Intelligence (Financial Services Best Practice)
Structure deal flow, investment materials and portfolio data into usable systems. Improve how documents, research and insights are stored and accessed. Apply consistency, auditability, and comparability across the investment process using financial and legal services / legal-engineering techniques such as document taxonomy, metadata tagging, structured drafting, clause or section extraction, integration with email systems.
AI, Workflow Automation & Practical Innovation
The objective is to use our existing technology and data investments to augment/quasi-automate key parts of our business and create durable competitive advantage. Identify and implement pragmatic uses of AI and automation (including LLM-assisted tools) in document analysis, research and workflow automation. Define and maintain responsible AI usage principles for an investment firm context, supporting professionals' judgement, not full automation.
Engagement & Continuous Improvement
Actively identify inefficiencies and process bottlenecks to remove friction from day-to-day workflows. Identify training needs and lead workshops to ensure fast adoption of practices. Work directly with the team to drive usage and behaviour change. Proactively identify and embed emerging best practices in AI, data and workflow automation in VC and financial services.
What we are looking for We are looking for commercially minded candidates who can build, deliver and drive adoption. Candidates often come from backgrounds such as
Requirements
Significant experience in technology, data, analytics or digital transformation roles. Familiarity with VC/PE fund operations and LP reporting cycles, and comfort with ambiguity. Track record in document-heavy, knowledge-driven environments (e.g., Venture Capital/Private Equity, legal, consulting, financial services). Hands-on experience with data platforms, BI tools, document management systems and automation tools. Comfort with change management and influencing adoption across a team of experienced investment professionals who may not be naturally tech-forward. Track record of delivering solutions that are adopted by users and provide clear business value, rather than purely technical outputs. Experience with AI/automation tools (including LLM-based tools) and implementation in real workflows or business contexts. Experience building and working with IT budgets in a lean environment. Comfortable working directly with senior stakeholders, prioritising work and delivering practical solutions. Experience from financial data, financial services, legal services or other professional services industry is desirable.