Product Data Analyst

BILL•San Jose, UT

19h•$81,300 - $114,400•Remote

About The Position

Innovate with purpose At BILL, we believe in empowering the businesses that drive our economy. By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations. And we don’t stop there: we’re creating the future of financial automation so businesses can spend more time on what matters. Working here means you become part of a vision-driven team that’s ready to tackle challenges and build cutting-edge solutions. We value purpose, drive, and curiosity—and we thrive in a fast-paced, ever-changing environment. Whether in one of our offices in San Jose, CA, Draper, UT, or working remotely, BILLders collaborate to deliver real impact for businesses that need more time in their busy weeks. BILL builds high performing teams and we seek to hire the best talent for every role. We're committed to building a workplace that fosters inclusion and diverse perspectives, valuing each person’s unique skills and experiences. We’d love to hear from you—you might be just what we’re looking for, whether in this role or another. ✨ Let’s give businesses more time for what matters. Make your impact within a rapidly growing Fintech Company As part of the Data Science & Analytics team, you’ll turn BILL’s rich product data into insights that power smarter decisions across our Spend & Expense product. In this role, you’ll partner closely with Product & other cross-functional teams to support Tier 1 initiatives with robust metrics strategy, analytical frameworks, and actionable deep dives. You’ll work hands-on with large, complex datasets to answer critical product questions, quantify opportunities, and measure the impact of features tied to our growth priorities.

Requirements

Bachelor’s degree in a quantitative field (Math, CS, MIS, or Economics) and 3+ years of experience in analytics or data science; a Master’s degree is a plus.
A solid analytical foundation, with experience working in a quantitative discipline (such as analytics, data science, business intelligence or a related field) or equivalent practical experience.
Advanced Proficiency in SQL & business intelligence/visualization tools, i.e., Tableau or Looker
Hands-on Experience with DBT for building & maintaining production-grade analytical models, including tests, documentation & Git-based workflows
Experience applying analytical and statistical techniques (such as descriptive analytics, cohort analysis, and A/B testing) to answer product or business questions and drive decision-making.
Strong communication and data storytelling skills, with the ability to translate ambiguous stakeholder questions into structured analyses and distill complex results into clear, compelling insights for product partners.
A collaborative, growth-minded approach, with a bias toward learning, iterating, and building trusted partnerships with Product, Engineering, Design, and other cross-functional teams.
Please note that this position is not eligible for visa sponsorship. Applicants must have authorization to work in the United States without requiring visa sponsorship now or in the future.

Responsibilities

Partner with Product, Engineering, and cross-functional teams to translate business questions across Tier 1 product initiatives into clear analytical plans, metrics, and experiment designs.
Query and analyze large, complex datasets to uncover trends in customer behavior, funnel performance, and feature adoption, transforming findings into actionable recommendations for product teams.
Build, maintain, and iterate on dashboards and recurring reports that track key product KPIs, highlighting trends, anomalies, and areas for deeper investigation.
Contribute to goal setting and forecasting for key product initiatives, partnering with product managers to size opportunities and model expected impact on growth and engagement.
Validate data quality and consistency for your product domains by collaborating with data engineering on instrumentation, documentation, and clear metric definitions.
Communicate insights through clear narratives, visualizations, and presentations that make complex analysis easy to understand for non-technical stakeholders and influence product direction.