Analyste de données principal modernisant les plateformes et pipelines de données d’EDC, société financière publique canadienne. Traduction des besoins opérationnels en modèles, règles de transformation et récits utilisateurs.
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
Requirements
- 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.
Benefits
- 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
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