VP – Analytics Engineering

Key responsibilities

• Lead the technical direction and delivery of a new Analytics Engineering capability.
• Act as the go-to technical voice across architecture, data modelling, engineering patterns and platform design.
• Lead a small engineering team from a technical and delivery perspective.
• Remain close enough to the code and architecture to review, challenge and improve engineering decisions.
• Work directly with senior business and investment stakeholders to understand problems and translate them into scalable technical solutions.
• Build trusted data products across complex financial, investment and insurance datasets.
• Establish strong standards around data quality, reconciliation, lineage, testing and governance.
• Design scalable integrations, data models and production engineering workflows.
• Help shape how AI and automation can improve engineering delivery and data workflows.
• Play a major role in the future structure and growth of the team.

What we’re looking for

• Strong software engineering or data engineering foundations.
• Experience leading a relatively small engineering team while remaining technically credible.
• Strong SQL, Python and data modelling skills.
• Experience building and owning production data platforms or data products end to end.
• The ability to challenge engineers on architecture, modelling, pipelines and production design.
• Strong stakeholder skills and the confidence to operate with senior non-technical audiences.
• Experience taking an unclear business problem and working out the right technical solution rather than simply executing a specification.
• Background in a strong engineering environment such as fintech, insurtech, insurance, investment management or financial services.

Particularly relevant backgrounds

• Experience across any of the following would be highly attractive: Insurance / insurtech, Insurance investment or asset-management data, Fintech, Private credit / direct lending, Private markets, Fixed income / institutional credit, Investment management, Reinsurance / annuities.
• Direct insurance or private-credit experience is advantageous, but strong engineering foundations are more important than having worked in one exact domain.

Technology environment

• Python
• SQL
• Snowflake
• dbt
• APIs / integrations
• Modern orchestration tooling
• CI/CD
• Cloud data platforms
• Data modelling
• Data quality, reconciliation and lineage
• Heavy dbt experience is not essential at VP level. Strong engineering fundamentals and the ability to quickly understand and challenge a modern data stack are more important.

Additional information

This could be particularly interesting for someone who has spent the last few years leading a team of around 4–10 engineers and is now ready to step into broader responsibility without moving away from the technology.

A background combining some consulting or client-facing experience with subsequent hands-on engineering in a fintech, insurtech or financial-services business would also be highly relevant.

They do not need a career CDO who has spent years managing very large organisations.

They are looking for someone at the point in their career where this represents an exciting step up in responsibility.

£1,200–£1,500 per day, outside IR35.

Initial 6-month contract with strong potential to extend and develop into a longer-term opportunity.