You'll be the person who turns messy data into decisions people actually act on. You sit between product, ops and leadership — building the metrics layer, answering hard questions fast, and making the numbers impossible to ignore.
What you'll do
- Own dashboards and reporting for product, growth and operations teams
- Write and optimise SQL across large transactional and event datasets
- Run deep-dive analyses: funnel drops, cohort retention, A/B test readouts
- Define metrics, document them, and keep a single source of truth
- Partner with engineers on tracking, data quality and pipeline fixes
- Present findings to non-technical stakeholders with clear narratives
Must have
- Strong SQL — joins, window functions, CTEs, performance basics
- Excel / Google Sheets at an advanced level (pivots, lookups, modelling)
- One BI tool: Power BI, Tableau, Looker or Metabase
- Statistics fundamentals: distributions, significance, sampling bias
- Ability to explain a chart to a CEO and a query to an engineer
Nice to have
- Python (pandas, matplotlib) or R for analysis
- dbt, Airflow or any data-modelling / orchestration exposure
- Experience with product analytics tools (Mixpanel, Amplitude, GA4)