About The Company
Scrambly is one of the fastest-growing adtech startups in the world, featured in the AppsFlyer Performance Index and Singular ROI Index in just 3.5 years.
We’re growing 250%+ YoY across revenue, team, product, and technology — fully bootstrapped and profitable.
We’re building the future of app discovery: a reward-powered alternative to the App Store and Google Play. Our loyalty-driven ecosystem connects millions of users with the world’s top apps and delivers unmatched, ROI-focused growth for mobile advertisers.
Our mission is to build a true alternative to traditional app stores — fueled by rewards and data — and create a new, performance-first growth engine for mobile app advertisers.
About The Role
We are looking for a Senior Analytics Engineer to build and run the data platform behind the commercial side of Scrambly — the pipelines and models that sit underneath how we spend, how we reward, and how we protect the ecosystem. These are decisions worth real money, made daily, on numbers you produce.
The role is half building and half answering. You will maintain the pipelines that turn raw events, attribution data, and advertiser feeds into tables people trust, and you will use those tables yourself — sizing opportunities, setting thresholds, and telling the business what the data says. We will go through the specifics with you during the process.
This is an early hire on a data team being built from scratch. You will work day to day with the Head of Data, alongside Data Scientists as they join, and in close partnership with Backend, User Acquisition, and Account Management. Part of the platform already exists and part of it doesn’t — you will take on what is there, improve it, and build what is missing.
We are flexible on level. The title reflects the ownership and independence the role carries, not a hard experience bar. If you have around three years behind you and the scope below excites you, we would still like to hear from you — we will set the level, title, and compensation to match what you bring.
Key Responsibilities
- Build and model. Design and maintain analytical pipelines in Dataform on BigQuery, from source data through to the tables that teams and downstream models consume, including the datasets behind new products as we build them.
- Analyse and recommend. Answer commercial questions with data, and design and tune the rules and thresholds that sit behind day-to-day decisions. Turn findings into recommendations non-technical teams can act on.
- Take ownership. Take on established pipelines you did not write — work out how they behave, document them, and keep them running in production.
- Make the data trustworthy. Build the testing, assertion, and alerting layer on the models that drive commercial decisions, so we find out when something stops working before a stakeholder does. Investigate and fix issues when a number looks wrong.
- Raise the engineering bar. Manage warehouse cost and query performance, reduce duplication and manual work, and help establish good practice for data — code review, CI, documentation, release conventions.
- Collaborate. Work with the Head of Data on scoping and prioritisation, with Data Scientists on training data and getting models into production, with Backend on event contracts and automating the loop into product systems, and with commercial teams who use these numbers daily.
Requirements
- 3+ years in analytics engineering, data engineering, BI development, or a closely related role, with pipelines running in production. We care more about what you have owned than about the number.
- Strong SQL and solid analytical data modelling: layered warehouse design, incremental models, and comfort with messy real-world business logic.
- Analytical judgement — able to take an open commercial question, decide what to measure, and come back with an answer and a recommendation rather than a table.
- Google Cloud experience is strongly preferred, as our whole platform runs on it: BigQuery, Dataform, Cloud Functions, Cloud Scheduler, Pub/Sub, IAM. We will consider people from AWS or Azure who have done the equivalent — a cloud warehouse, a transformation framework (Dataform or dbt), and serverless functions and scheduling.
- Python for data work, and familiarity with Git and code review — plus an instinct to test data rather than assume it is correct.
- Curiosity and comfort with unfamiliar systems: willing to dig into code or data you did not write, work out what it does, and write it down for others.
- A proactive, self-directed way of working, and the confidence to ask questions and say when something does not look right. You will be in a very small team.
- English at B2+, written and spoken — you will document systems others rely on and work daily with an international team.
Nice to Have
- Adtech or performance marketing data — attribution and MMP data, cohort LTV or ROAS, campaign cost and revenue reconciliation.
- Fraud, anomaly detection, or trust & safety data work.
- Event-driven or near-real-time pipelines — Pub/Sub, Eventarc, Kafka, or serving data through a low-latency store such as Redis.
- Experimentation (A/B design and readout) or supporting production ML (feature pipelines, model monitoring).
What We Offer
- Variety — you will build the pipelines and use them to answer the questions that matter, rather than only one or the other.
- Work directly attached to revenue, with visible impact on the business within weeks.
- A close working relationship with the Head of Data, a real say in how the data function is built, and room to grow into a senior or lead role.
- A level and compensation matched to what you bring, rather than to the title on the advert.
- A profitable, bootstrapped company growing 250%+ YoY, where investment in data is justified by results rather than budget cycles.