Why This Job is Featured on The SaaS Jobs
This Business Intelligence Engineering Manager role stands out in SaaS because it sits at the intersection of product usage, revenue motion, and go to market decision making. In a product led environment like Dropbox, the ability to translate behavioral data into shared metrics and trusted reporting is foundational to how teams prioritize roadmap work, evaluate adoption, and understand retention signals.
For a SaaS career, the long term value comes from building systems that make data usable across functions, not just producing one off analyses. Managing BI engineering in this context typically means shaping the definitions, pipelines, and dashboards that become the operational language of the company. That experience transfers well across subscription businesses where experimentation, funnel measurement, and lifecycle reporting are core to growth and optimization.
This role is best suited for someone who enjoys cross functional partnership and can balance technical rigor with business clarity. It will appeal to professionals who like turning ambiguous questions into structured measurement, and who want to influence how multiple teams consume data without being tied to a single product area.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
The Dropbox Data Science team transforms data into powerful insights that inform everything we do at Dropbox. Combining applied analytics techniques with deep business insights, we investigate user behavior, product performance, and market trends to uncover new opportunities for growth, optimization, and innovation. Our work is highly collaborative: we work closely with Revenue, Product, and Marketing teams to enable data-driven development and personalized customer solutions. It’s also highly creative, as we experiment to develop tools and dashboards that democratize insights across Dropbox. If you are driven by solving challenging problems and using the power of data to deliver impactful, user-centered products and services, join our Data Sciences team.
Areas of work include Applied Analytics, Experimentation, Data Engineering, Machine Learning, Business Intelligence, Data Visualization, and Statistical Modeling.
