Senior/Staff Data Analyst

At Future, we believe that personal connection is the key to progress. Our mission is to make world-class coaching accessible to everyone, helping people unlock their full potential through expert guidance, motivation, and accountability. As a digital personal training platform, we deliver highly personalized coaching, tailoring workout plans and support to each individual’s goals - all through a seamless app experience.

Since launching in 2017, Future has grown from a brainstorm in a San Francisco cafe into the nation’s largest provider of personal training sessions. In January 2025, Future announced its merger with Autograph, the company founded by 7x World Champion and entrepreneur Tom Brady. We’re poised for massive growth as we expand our brand, forge new partnerships with some of the world’s most iconic athletes, and harness AI to enhance and scale our coaching experience.

As we continue to grow, we’re investing in cutting-edge technology, deepening our roster of elite coaches, and building new partnerships. If you're passionate about shaping the future of fitness, come join us - we’re just getting started.

We're hiring a Senior/Staff Data Analyst to define and lead the next phase of our analytics practice. You won't be inheriting a dashboard library or a backlog of ad hoc requests. You'll be building the data foundation from the ground up — shaping how we instrument our product, measure our business, and make decisions at every level of the company.

This role requires someone who thinks in systems and works the full stack. You'll move fluidly between data engineering, analysis, and stakeholder strategy — owning the end-to-end process rather than handing off between teams.

• Define how Future measures what matters — from product health to business performance to coaching outcomes.
• Build relationships with data consumers across the organization to develop a clear point of view on what our data products should do and how they should be designed.
• Own the analytics roadmap. Decide what to build, what to instrument, and what to prioritize — with conviction, not just consensus.
• Develop and maintain a strong perspective on how data and AI should power decision-making as we scale.

• Design and implement data transformation pipelines in dbt to create clean, reliable, and interpretable datasets.
• Partner with the broader team to integrate and normalize data from 30+ disparate sources into a flexible, well-documented data model.
• Build for scale — write code and structure systems that the next analyst can pick up and extend without heroics.

• Perform strategic analyses on large, complex datasets and distill findings into clear, actionable recommendations for leadership.
• Analyze coaching outcomes, member engagement, and retention data to surface what drives lasting behavior change — and feed those insights back into the product and coaching experience.
• Build dashboards, reports, and visualizations that surface the metrics that matter and help stakeholders make better decisions faster.
• Define success metrics for product and business initiatives. Instrument everything. Kill what isn't working.

• Use AI to accelerate data exploration, synthesis, and documentation — then apply your judgment to sharpen the output.
• Bring a perspective on how AI should be woven into our analytics practice itself — anomaly detection, automated reporting, intelligent alerting.
• Raise the AI fluency bar for the team. Share what's working. Push others to adopt.

• 7+ years of experience as a data analyst, analytics engineer, or data scientist.
• You've built analytics practices at a startup or early-stage company — you know what it means to instrument from scratch with limited resources and no established playbook.
• Deep fluency in SQL. Comfortable reading and writing Python.
• 1+ years of hands-on experience with dbt, Snowflake, or equivalent in real-world production environments.
• Proven ability to own full-stack analytics projects end-to-end: data engineering, analysis, dashboarding, and stakeholder reporting.
• You've operated at the Senior or Staff level — or functioned at that altitude regardless of title.

• Strong analytical instincts — you pull your own data, form hypotheses, and let evidence shape your decisions.
• Clear, precise communicator. You can translate complex findings into plain language for a non-technical executive as easily as you can go deep with an engineer.
• Comfortable with ambiguity and pace. You don't wait for perfect data or perfect requirements to move forward.
• Strong project management skills — you can manage several workstreams at once without dropping the ball.
• Low ego, high ownership. You do what needs to be done.

• Experience in health, wellness, consumer health, or marketplace businesses.
• Experience building or scaling a data practice through rapid company growth.
• Experience building AI-native analytics tooling or workflows.
• Familiarity with product analytics platforms (Amplitude, Mixpanel, etc.) and experimentation frameworks.