Data Product & Analytics Engineering Senior Manager

About Moneybox

Role overview

Team leadership and roadmap

• Manage, coach and develop four Analytics Engineers, providing technical direction while supporting engineer ownership.
• Guide modelling and architecture decisions, encourage reuse and simplification, and align solutions with target data architecture.
• Maintain a prioritised roadmap for models, marts, dimensions, attributes, governed metrics and architectural improvements.
• Develop centralised, trusted and reusable data models that reduce duplicated transformation logic.

End-to-end data products

• Own data products from problem definition and discovery through architecture, prioritisation, delivery, quality assurance, adoption and ongoing ownership.
• Coordinate dependencies across Product Engineering, Data Engineering, Insights, Reporting and other teams.
• Identify upstream changes and ingestion needs early to support downstream data delivery.
• Determine whether stakeholder needs call for a new or improved attribute, metric, model, mart or data product.

Essential experience and skills

• At least 5 years of experience in data architecture and analytical data modelling.
• Experience with modern transformation frameworks such as dbt and cloud data platforms such as Databricks, Snowflake or equivalent.
• Experience leading, mentoring or technically guiding Data or Analytics Engineers.
• Understanding of the data lifecycle from source applications and ingestion to transformation, semantic modelling and consumption.
• Experience managing roadmaps and prioritising competing business, architectural and technical requirements.
• Able to communicate with engineers and senior business stakeholders.

Candidate profile and desirable experience

• Previously hands-on Data Engineer and/or Analytics Engineer with experience translating business problems into scalable data products and technical requirements.
• Comfortable with ambiguity, autonomy and changing briefs in a fast-paced environment.
• Desirable: data quality, testing, lineage, documentation, ownership and governance experience.
• Desirable: regulated-industry experience, ideally financial services or fintech.
• Desirable: Databricks and dbt experience, especially centralised transformation or modelling layers.
• Desirable: managing dependencies across engineering and analytics teams, including upstream application or event-data changes.
• Candidates meeting most, but not all, essential criteria are encouraged to apply.