Data Product & Analytics Engineering Manager

About Moneybox At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money, it's about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement.

Job Brief

We’re looking for a Data Product & Analytics Engineering Senior Manager to join the Data & Insight team on a permanent basis. This role is dedicated to lead a team of four Analytics Engineers and own the roadmap for the development of our analytical data products. 

You will be responsible for identifying, shaping and prioritising the data capabilities we need, orchestrating their delivery across multiple teams, and leading the Analytics Engineers who will design and implement them. This role combines Data Product Management, Analytics Engineering leadership and data architecture. 

What you'll do

Lead the Analytics Engineering team 

• Manage and develop a team of four Analytics Engineers across different levels of seniority. 

• Provide technical direction, coaching and context while creating an environment where engineers retain ownership of solution design and implementation. 

• Work with the team on modelling and architecture decisions, challenge proposed approaches, identify opportunities for reuse and simplification, and ensure solutions align with our target data architecture. 

• Coach engineers at different levels, helping them improve not only their technical capabilities but also their understanding of business problems, architectural thinking and ownership of data products. 

Own the Analytics Engineering roadmap.  

• Define and maintain a prioritised roadmap covering new data models, data marts, dimensions and attributes, governed metrics, and architectural improvements.

• Drive the transition towards more centralised and trusted data models, reducing duplicated transformation logic and creating reusable representations of core business entities, facts, dimensions, metrics and relationships. 

Own data products end-to-end.   

• Take requirements from problem definition through discovery, architecture, prioritisation, delivery, quality assurance, adoption and ongoing ownership.