Senior Data Analyst, Lifecycle
Finom is hiring for the position of Senior Data Analyst, Lifecycle in Germany. The listing describes it as a full-time, senior position with hybrid working.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Finom is hiring for the position of Senior Data Analyst, Lifecycle in Germany. The listing describes it as a full-time, senior position with hybrid working.
The advert mentions Python, SQL, Tableau, Power BI, Git, statistics and CRM among the skills and tools for this role.
The employer does not state a salary in this listing; DGJ Insight below shows an official pay benchmark for the place where one exists.
Benefits mentioned in the advert: equity or stock options and learning budget.
Visa sponsorship is not mentioned. Applicants from outside Germany should check the official work permit route before applying.
Dev Global Jobs found this job on Arbeitnow on Nov 5, 2026.
- Employer
- Finom
- Location
- Germany
- Work mode
- hybrid
- Job type
- Full-time
- Level (from the title)
- senior
- Pay
- Not stated by the employer
- Found
- Nov 5, 2026 on Arbeitnow
- Employer text
- 930 words, about 4 min to read
- Apply only on the official page (arbeitnow.com). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (Python, SQL, Tableau and Power BI) in your CV where you really have them; applicant tracking systems look for these words.
- Read the employer’s full text below (about 4 min) before you apply.
- Build or check your CV with the free DGJ CV Builder.
DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
- EU Blue Card: Recognised degree and a job offer above the salary threshold
- Skilled worker visa (Fachkräfte): Recognised qualification and a job offer
- Opportunity Card (Chancenkarte): Points-based job search for up to one year
Official routes: Make it in Germany (Federal Government portal)
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
01About 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 Do02What 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 Bring03What 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.
04Nice 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.
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