About the role

Data Analyst linking Float's data infrastructure to business decisions for strategic insight. Collaborating with teams to create metrics and governance for effective decision-making.

Responsibilities

Design, build, and maintain Float's core dashboards and reporting infrastructure — audit for quality, assign ownership, archive stale content, and set the standard for what good looks like
Work closely with the Analytics Engineer to translate business requirements into data models — write clear specs, validate outputs, and drive metric definition alignment across stakeholders before anything gets built
Use AI as a force multiplier — not just for drafting SQL, but for building self-serve tooling, scaling analysis and making BI more accessible to non-technical stakeholders. You bring the business context that makes AI outputs actually useful, and you know where the guardrails need to go.
Define and maintain Float's authoritative metrics library — what they measure, how they're calculated, and who owns them; surface and resolve discrepancies when Finance and RevOps aren't working from the same number
Partner with the Chief of Staff and Head of Data on QBRs, board prep, and performance reporting; turn leadership questions into clean analysis, and run exploratory deep dives to answer specific questions
Build a self-serve BI environment that reduces dependence on the data team — verified dashboards, documented metrics, and Metabase collections that Finance, Ops, Support, and Sales can navigate without filing a ticket
Flag data model gaps and quality issues to the analytics engineer; help prioritize infrastructure work based on business impact

2–4+ years of experience as a BI or business analyst, ideally at a high-growth fintech or SaaS company
Strong SQL — CTEs, window functions, aggregations; you write clean queries and can explain them to a non-technical stakeholder
Deep experience in a BI tool (Metabase, Looker, Tableau, or Sigma) — you've owned a BI environment.
Comfortable reading dbt models and understanding data lineage; prior dbt experience is not necessary but ability to contribute to dbt project is expected
Comfortable using AI tools as an accelerator — LLMs for SQL drafting, documentation, or structuring analysis; you know where they're useful and where they need guardrails
Structured thinker who turns ambiguous questions into clear analytical frames — and doesn't wait to be handed one. You spot what's worth investigating and drive it yourself.
Strong written communication — your analysis only lands if the narrative does
Proactive and collaborative — you share context before being asked and work with the analytics engineer, not around them
Genuinely curious about the business, not just the data
Experience supporting a senior audiences is a strong plus
Familiarity with Snowflake or a similar cloud warehouse is a plus
Python experience is a plus, particularly for automation or analysis outside of SQL.

Competitive compensation, equity options, and benefits
Hybrid work model – we are based in Toronto with in-office days for connection and collaboration
Enjoy catered team lunches every Tuesday, Wednesday and Thursday
Bring your pup to our dog-friendly office
Thrive in a high-trust, high-performance culture where your work truly matters