Data Analyst, Product

The team

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

We’re looking for a Data Analyst, Product to join our team remotely. The #1 goal of this role is to turn complex data into clear, actionable insights that improve user experience, support the development of new features, deepen engagement, and help leadership make high-impact product decisions.

You’ll own end-to-end data management from production-grade pipelines and dbt models to dashboards and metric frameworks for the Pro product team. You'll define north star metrics from the ground up, drive experimentation infrastructure through A/B testing and causal inference, and translate results into clear product recommendations. We're actively building a culture where AI tooling is part of how we work, from LLM-augmented pipelines to GenAI-assisted workflows, and we're looking for people who are excited to grow alongside that.

Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with deep knowledge of client retention funnels and user behaviour. You bring experience from environments where data quality has real consequences and where you've had to build the infrastructure, not just use it.

The opportunity

• Operate as a full-stack data analyst within the Pro team, owning your domain completely while collaborating closely with colleagues across the pod.
• Own the design and evolution of dashboards, north star metrics and analytical frameworks that drive decisions at the highest level of the business.
• Build and maintain data infrastructure at scale, from scalable dbt models and production pipelines to full-funnel reporting that powers cross-functional teams.
• Lead experimentation across the Product domain by designing and owning A/B testing frameworks, applying causal inference techniques and turning results into clear, confident recommendations that influence product strategy.
• Influence technical direction across the data team, contributing to how we build, what we prioritise and how we raise the bar on data quality and engineering standards.
• Embed AI tooling into your workflow in ways that have tangible business impact, including LLM-augmented pipelines and GenAI-assisted analytics workflows.
• Deliver insights through clear, data-driven storytelling to technical and non-technical audiences, including senior leadership.

What You Bring

• 7+ years of experience in data analytics or analytics engineering, ideally within fintech, payments, financial market or crypto where data quality and scale are non-negotiable.
• You have hands-on experience with advanced trading products and solutions in equities, crypto, or other assets. You either have a professional background working within financial markets or on a trading desk, or you are a professional trader.
• Hands-on experience building and owning production data pipelines, with strong familiarity with dbt and Airflow or equivalent orchestration tools.
• Full mastery of SQL including complex joins, CTEs and analytical functions, alongside strong Python proficiency for pipeline development and analysis.
• Hands-on experience designing and running A/B tests and experimentation frameworks, with the ability to apply causal inference techniques and translate results into clear growth recommendations.
• Demonstrated experience leading cross-functional data initiatives from design to delivery, with measurable business outcomes you can point to.
• Strong communicator who can simplify complex data ideas for both technical and non-technical audiences.
• A degree in a field emphasising analytical rigour such as software engineering, economics or a hard science.
• Based in Canada, the US, the UK or the EU and fluent in English.

Nice to haves

• Experience with LLM-augmented analytics pipelines or AI-assisted workflows in a production environment