Senior Data Analyst, Lifecycle

Finom is a European tech startup headquartered in Amsterdam, and we’re on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform.

We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, HubSpot, KAYAK, and Stripe.

Finom's platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We're actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain.

At Finom, we’re not just redefining the entrepreneurial experience — we’re empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and our business as a whole.

Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and, of course, our business.

About the role

We are looking for a Senior Data Analyst, Lifecycle to own measurement and experimentation for how new customers activate and existing ones stay.The metrics are yours whatever the lever: CRM campaigns, promos and incentives, onboarding changes in the product, and a next best action layer that decides what each customer should see next. You decide what we test, how we test it and what we do with the answer. You also bring your own hypotheses on where the next win is.

What you'll do

Join activation and retention initiatives before anything is built, whether they come from CRM, growth or product. Define the primary metric, check against historical data whether the effect is detectable at our volumes, and turn solution-shaped requests into problem-shaped ones.

Design and analyse experiments end to end: hypothesis, guardrails, audience split, MDE and power, stopping rule, and a readout that ends in ship, iterate or kill.

Correct for channel and engagement bias (not everyone opens email, and open and click populations are self-selected). When a clean A/B is impossible, use causal inference: diff-in-diff, matching, synthetic control or geo designs.

Bring hypotheses of your own. Launch a few new experiments across onboarding and the customer lifecycle, and turn one-off wins into recurring segmented flows.

Build deliberately simple models: segmentation, propensity, churn and dormancy scoring. They feed audiences, personalisation and incentive targeting. Over time these come together into next best action: for a given customer, which offer, feature nudge or message to send next, through which channel, and whether to send anything at all. You own the logic and the measurement of whether it beats the current rules.

Automate your own recurring work: audience refresh jobs, readout templates, dashboards for Activation, Onboarding and Retention. Ship models the Databricks way (scheduled jobs write tables, tables map into downstream tools such as Braze) and bring engagement data back into the warehouse to close the loop.

What you bring

Strong SQL and Python, and a Git-based warehouse workflow with code review.

Experiment design, statistics and metric design you can defend.

BI experience, Power BI/Tableau or similar.

Stakeholder management and business sense: you can push back, reframe a request and land a recommendation with a non-technical audience.

Curiosity and ownership. You run your area without waiting to be asked.

Nice to have

Causal inference methods and a clear sense of when each one is valid.

Clustering and segmentation that ends up in use, not in a slide.

Predictive modelling with honest validation. Uplift modelling is a plus.

Braze or a comparable CRM platform: audiences, canvases, native split testing, channel mechanics and their biases.