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About Us and Why We’re HiringYNAB (“why-nab”) is a product company with a mission to help people get good at money so they never worry about money again. Join a tiny but mighty team of two. Our “data friends” help people in every department make decisions more confidently. This is a hands-on role that covers the full spectrum of data work, from technical to strategic—you’ll transform data into clear, opinionated recommendations and help shape what we do next. You’ll also build systems that allow others at YNAB to self-serve with confidence.For this role, you mustLive in the United States, and be authorized to work there without sponsorship.Have at least 3 years of experience working in the data field in a product-driven environment.Have experience with some Modern Data Stack tools (e.g. Snowflake/Databricks/BigQuery, dbt, Fivetran/Airbyte, Amplitude/Mixpanel, Metabase/Looker/Tableau).Have experience writing and maintaining SQL in a production environment (e.g., dbt models, shared queries, dashboards).Be confident in and passionate about using AI to do your job better, while knowing what is worth not outsourcing.Preferred QualificationsExperience at a small-to-mid size company, where you wore multiple hats and worked without a dedicated data engineering team beneath you.Experience working across multiple stakeholder groups.B2C subscription/SaaS experience.Experience designing and analyzing A/B tests.Ultimately, your role is to be an enablerYou’ll help people in every department determine the “right” questions to ask, define good measurable outcomes, and evaluate all the steps of an initiative cycle—from research and planning to post-launch evaluation. The deeper goal is to build systems, definitions, and self-service paths that let people get to trustworthy answers.You’ll also help the data team tackle key goals: strengthen foundations; increase and evangelize data literacy; and shape what AI in data looks like at YNAB. Your job will start out at about 60% foundational and operational work and 40% strategic insight and opinionated recommendations.How You Show Up for Teams Across YNABYou’re seen as a thought partner, not a service function.You proactively point teams toward where data indicates opportunity.You challenge the question before building the answer, testing theories instead of confirming them.You figure things out and ask for help before you’re truly stuck.You form a point of view and pair analysis with recommendations.You flag when a question needs qualitative input, not another query.You have a bias toward action—shipping good-enough work beats waiting for perfect.Where You’ll Focus with the Data TeamStrengthening our foundationsYou own data quality end-to-end (hands-on role; you’ll be in the data every day).You co-own the operational health of the data stack: Snowflake administration (permissions, cost monitoring, troubleshooting), dbt model development and maintenance, Fivetran/Airbyte pipeline monitoring, and dashboard building.You make systems reliable—fixing the data model so the next ten questions are easier to answer.You treat ad-hoc requests as a signal and fix root causes behind recurring questions.Increasing data literacyYou explain complex topics so non-data folks leave more confident than confused.You meet stakeholders where they are (Amplitude users, learners, or Slack-summary readers).You create self-service tooling with results people can trust.You give thoughtful feedback on dashboards, experiment analysis, and peer dbt SQL code.You share insights, wins, losses, and what you learned—supporting a culture of experimentation and learning.Shaping what AI in data looks like at YNABYou help define where AI generates the most value in data work and where human judgment must be protected.You’re AI-fluent: you’ve used these tools enough to know where they shine and where they hallucinate, and you can explain both to skeptical and over-eager teammates.What Success Looks LikeFoundations are visibly stronger (clearer metric definitions, more reliable key tables, experiment frameworks in place).The data stack runs day-to-day, with issues resolved before others notice; dbt models are proactively maintained and improved.Data discussions focus on trade-offs and decisions, not just correctness.The ad-hoc request queue shrinks because people answer their own questions using tools and processes you built.You proactively surface insights that inform strategic decisions.You’re the Person We’re Looking For IfYou can turn vague business questions into appropriate analytical approaches.You have experience influencing decisions, not just informing them.You have data-quality intuition from working with messy data.You can catch when AI-generated SQL silently drops records or makes wrong assumptions.You use AI fluently while owning output and maintaining judgment about correctness.You treat data work as a craft, reviewing dbt PRs carefully and pushing back on your own analysis.You’re motivated by progress and ship good-enough analysis on a practical timeline.You create lightweight processes that help teams scale—and retire them when they’ve outlived usefulness.You own outcomes, even when the path isn’t clear.You match analysis depth to decision weight.Competitive CompensationThe starting salary for this role ranges from $85,000 - $110,000 USD annually. You’ll also be eligible for an annual raise and profit-sharing twice a year.
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