Data Engineering & Analytics Manager

Job description

Insurance is a numbers business, and most of those numbers are harder to trust than they look. You would lead the team that defines the numbers Insify runs on, from the event a customer fires on our website to the report a risk carrier audits months later. About the Role You would lead our Data & Analytics team of 3-4 engineers end to end: ingestion, the warehouse and its models, the metric layer, and the analysis and reporting built on top of it. It is a small team with a big surface, so there is nowhere to hide and nothing you can't reach. We are deliberately not splitting data engineering from analytics. At our size the hard problems sit in the handover between the two, and we want one person who enjoys that whole puzzle. The team serves four groups with genuinely different needs. Growth wants hourly to daily signals and can live with approximately right. Carrier reporting and corporate finance are calendar-locked and need numbers that reconcile and survive an audit months later. Actuarial needs depth and lineage over speed. Prioritising here is arbitration between things that fail in different ways, and the arbitration is yours. It's a hands-on role. You can open the warehouse and read the models, you review a new data model before it ships, and your team trusts your technical judgement because you still use it. The job is still the team: its own game, its communication, and each person in it. When something slips, hands-on work is the first thing that yields. What you'll be doing Leading a team of 3-4 data engineers and analysts end to end: what it builds, how it runs in production, and who you hire and grow to get there Running discovery with the teams we serve: understanding what they need from data, and bringing the platform and datasets that enable them Owning the trade-offs between Growth, carrier reporting, finance and actuarial: deciding what the team works on, driving alignment with key partners across other teams, and saying no when the answer is no Making the platform scale: a new country, carrier or product should mean configuration and reused models, and recurring manual work, like carrier reporting and cash reconciliation, should turn into automation Bringing engineering excellence to how the team works: data contracts with product teams, testing in CI, alerts that get resolved, a way to try changes before they hit production Challenging o…