Your mission
As our Analytics Engineer, you’ll help define how we measure the business.
You’ll work closely with product, monetisation, UA, and finance leads to turn ambiguous questions into clear models and reliable metrics. Your job is to make sure our mobile KPIs are accurate, explainable, and most importantly trusted.
When leadership asks:
- Why did Week-2 retention drop?
- Is this cohort actually profitable?
- Can we safely scale this campaign?
- Did the pricing experiment improve LTV?
You make sure the answer comes from clean modeling, not "gut feel." This role is a 50/50 split between building elite data infrastructure (dbt + BigQuery) and digging into the numbers yourself when something looks "interesting."
What you'll own
- Stakeholder partnership: Sit with product, finance, UA, and monetisation leads to translate fuzzy business questions into precise analytical requirements.
- Data modelling: Design and maintain the dbt project on BigQuery - dimensional models (fact/dim), incremental loads, reusable macros, thorough tests, and documentation that actually gets read.
- Dashboards & self-serve: Build and iterate on Metabase dashboards that give non-technical teams the confidence to make decisions without pinging you for every question.
- Data analysis & deep dives: Go beyond building pipelines - dig into the data, investigate anomalies, run ad-hoc analyses, and surface insights that stakeholders didn't know to ask for.
- Data quality & reliability: Monitor production dbt runs, triage failures, trace issues from the dashboard layer all the way back to raw sources, and communicate impact and resolution to stakeholders.
- Mobile metrics expertise: Be the team's go-to on core mobile KPIs - DAU/MAU, LTV, retention cohorts, ARPU, ROAS - and ensure the models behind them are accurate and trustworthy.
