Senior/Staff Data Analyst

About Future

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.

About the Role

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.

What You'll Own

Analytics Strategy & Vision

• 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.

Data Infrastructure & Engineering

• 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.

Analysis & Insight

• 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.

AI as a Force Multiplier

• 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.