Finance & Data Analyst

You are good at what you do. You know how to reconcile a messy dataset, when a number deserves a second look and how to explain a discrepancy to someone who does not live in spreadsheets. What you are probably missing is influence over how that work is organised: the systems, the workflows, the good ideas that never make it out of an inbox.

At Insify, that changes. You work at the intersection of finance, accounting and data. You reconcile premium, commission and claims positions across our insurance carrier partners, build the dashboards and automations that keep our finance operations reliable, and flag anomalies before they become problems. You do this in a small, sharp finance team in a multi-entity, multi-carrier business, which means real responsibility from day one and direct contact with the finance and operations teams, and with leadership itself.

You get a complete picture fast, even when the data is imperfect, and you know when something needs to be driven to root cause and when it can be parked. You explain your findings clearly, to accounting, to operations and to the leadership team. And within your mandate you decide how you work: no bureaucracy, just your judgement and the freedom to act on it.

The best part of this role: you do not just report the numbers, you help redesign how the numbers come together. We use technology and AI to take the repetitive work out of your day (data extraction, first-pass pattern recognition, recurring workflows), so you can spend your time on the questions where a human really makes the difference: why does this number look wrong, what does it mean and what should we decide? You help determine what we automate, what we deliberately do not automate and how we strengthen our data quality and financial controls as the company grows. You build the solution, rather than handing it to another team.

And building means building. You write your own SQL to get to the data, your own Python to clean, reconcile and automate, and you use Claude and other AI tools as a matter of course to get there faster: drafting a query, debugging a script, turning a one-off analysis into a repeatable workflow. You do not need to be a software engineer, but you do not wait for one either. Where something belongs in our production systems, you are the finance-side liaison to our data team and engineers: you translate finance's reporting needs into requirements they can build against, and translate what is technically possible or already available back to finance.