The role 👤

Join our data team and help us make sense of the chaos (in the best way possible).

We're looking for two Data Analysts who can turn messy data into clear stories, spot patterns before they become problems, and communicate insights without putting stakeholders to sleep.

You'll work with our existing team of junior-to-mid analysts, bringing 3-5 years of battle-tested experience. You've seen enough dashboards to know when a metric is lying, written enough SQL to do it in your sleep, and presented enough findings to know that executives don't want 47 slides.

This isn't a "pull reports and call it a day" role. You'll be:

• Digging into our product data to understand what's actually happening vs. what we think is happening.
• Building analyses that change minds (and roadmaps).
• Partnering with product, marketing, and ops teams who depend on you to cut through the noise.
• Mentoring our junior analysts when they're stuck (or when you need a rubber duck).

You should apply if... ↪️

You have:

• 3-5 years as a Data Analyst or similar role - titles vary, skills matter more.
• Strong SQL skills - you can write complex queries without Stack Overflow open in another tab (most of the time). We use PostgreSQL and BigQuery.
• Statistical foundation - you understand hypothesis testing, can run and interpret A/B tests, know when correlation doesn't imply causation, and can explain confidence intervals to non-technical stakeholders.
• Analytics tool proficiency - Tableau, Looker, Power BI, or similar. We use Metabase, but if you're strong in one, you can learn another.
• Python or R experience - not necessarily expert level, but comfortable enough to wrangle data, run statistical tests, or build a quick model when SQL isn't enough.
• Spreadsheet wizardry - yes, Excel/Google Sheets still matter. You know when to use a pivot table vs. when to reach for SQL.
• Business intuition - you ask "why does this matter?" before diving into analysis, and can define success metrics and measurement plans before feature/project launches
• Communication skills - you can explain technical findings to non-technical humans without the jargon fog, and you can partner with engineers to ensure events/data are tracked in ways that support analysis and experimentation.

Bonus points for:

• Experience with our specific stack: PostgreSQL, BigQuery, Metabase, DBT.
• A/B testing or experimentation background.
• Experience in Finance, Product.
• Having actually used AI tools effectively.

You'll thrive here if you:

• Like figuring out WHY something happened, not just WHAT happened.
• Get energised by "here's a weird pattern, let me investigate" moments.
• Can handle ambiguity (our data is real-world messy, not Kaggle clean).
• Want to influence decisions, not just inform them.
• Believe good analysis is 30% technical skills, 70% asking the right questions.
• Think of AI as a copilot, not an autopilot.

This probably isn't for you if:

• You just want to build dashboards and never talk to humans.
• Ambiguous questions stress you out instead of exciting you.
• You need every requirement spelled out in perfect detail.
• You think "the data says X" without questioning if the data might be wrong.

#LI-DNI