About the role
In most traffic teams, analytics stops at the ROI column in a tracker. Not here: we have been in dating since 2011 and count money across the entire funnel — click → registration → subscription → rebill. The buyer bonus formula is open, and it runs on data you collect and validate. Your numbers are someone’s paycheck: when a buyer opens the dashboard at month-end, every cell has to be trustworthy. That trust zone is yours to build and keep. You report to the Head of Media Buying.
What you will do
- Stitch tracker and product analytics into one pipeline, so a click in the tracker finds its payment in the product.
- Build BI dashboards that cross spend, revenue and LTV — by source, campaign, GEO and each individual buyer.
- Run cohort analysis: conversion to subscription, retention, rebill dynamics, LTV forecasts 3–6 months out.
- Validate bonus calculations: reconciling the data before payout is your responsibility, not something that “got computed somewhere”.
- Host weekly reviews with buying teams: which cohorts pay back, which campaigns to scale, where LTV sags and why.
- Set up anomaly alerts: fraud patterns, tracker/product discrepancies, sudden conversion drops.
- Define with the tech lead which events and parameters tracking must pass so the data reconciles.
What matters to us
Required:
- 2+ years in performance or product analytics.
- Confident SQL plus at least one BI tool (Tableau / Looker / Metabase or similar).
- Hands-on cohort analysis and LTV forecasting — from real work, not a course.
- Unit economics of subscription funnels: why the rebill matters more than the first payment.
Nice to have:
- Python for pipelines and models.
- Trackers (Keitaro / Binom / Voluum) and S2S postbacks.
- A dating or subscription-product background.
What you get
- Fully remote work judged on output, not online status.
- Fifteen years of product data to work with — real cohorts, not a startup with no history.
- Direct visibility: buyers and the Head open your dashboard every morning.
- A tooling budget granted the moment you justify it with numbers.
- Salary paid on a fixed date, never “stuck for a few weeks”.
Growth track
Analyst → Senior Analyst → Head of Analytics with your own team as the department scales. One promotion criterion: the system you built runs without you steering it by hand.
Compensation
$1,800–2,800 base + result-based bonuses. Payouts on a fixed date.
Process
30-min screening → interview → compact test up to 3 hours (a real case on anonymized data) → offer.
Questions about this role
What is the current stack and what will I build?
A tracker and product analytics exist as separate systems. Your first task is connecting them into one picture: data pipeline, cohort reports, team dashboards.
Where does the data come from?
Two sources: the tracker (clicks, conversions, spend per campaign) and product analytics (payments, retention, LTV). The key job is making them reconcile.
Do I influence the bonus formula?
Directly: buyer bonuses are computed from data whose quality you own. It is a high-trust zone — which is exactly what makes it interesting.
How much ad-hoc vs systematic work?
Target ratio is 30/70. Early months will be more ad-hoc while dashboards are built; then self-service should cover most questions.
Do I need to know traffic arbitrage beforehand?
No. You need funnels and unit economics — we will explain arbitrage specifics (sources, campaigns, consumables). Half the jargon is learned in month one.