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Data Analytics Manager
LinkedIn Recruiter Post
Lead a team delivering high - impact insights across our project portfolio, transforming project data into reports and dashboards.
About the role
- Data Analyst position for a fintech company focusing on product analytics within banking and payments. Driving measurement frameworks and data quality initiatives to support product features.
Responsibilities
- Define what needs to be measured before features ship and support the instrumentation to measure it. You translate roadmap decisions into measurement requirements.
- Design and get broad alignment on measurement frameworks before features go to engineering — hypothesis, cohorts, success criteria.
- Design and analyze experiments - structure A/B tests for product changes. Define the hypothesis, select the right unit of randomization, size the experiment for sufficient power and interpret results with statistical rigour
- Own the metrics and dashboards for Banking and Payments. Every number is traceable to a reliable dbt model. You keep them accurate.
- Own data quality in your product area — audit dbt models, write clear briefs for analytics engineering, flag gaps before they become problems.
- Run cohort and retention analysis natively — activation sequences, behavioral funnels, leading vs. lagging indicators. Diagnose which lever moves the number, not just what happened.
- Debug ambiguous numbers to the source — pipeline issue, definition mismatch, or real product signal. You close the loop.
- Write expert SQL and bring statistical rigour — cohort construction, retention calculations, confidence intervals, significance testing. You know when you have enough signal to act.
- Use AI as a multiplier — accelerate analysis, generate first-pass SQL, and pressure-test hypotheses faster than you could alone. You know how to give a model the business and data context that makes its outputs impactful.
- Take your share of ad-hoc requests on rotation — owning your product area gives the broader data team meaningful breathing room.
Requirements
- 2–4+ years in product analytics, ideally embedded in a product team at a high-growth fintech or SaaS company.
- Product analytics depth — you've been embedded in a product team, own activation funnels and cohort retention natively, and know how to instrument a feature before it ships.
- Experimentation and measurement design — you design A/B tests and quasi-experiments from the ground up: hypothesis, randomization unit, sample size and power calculations, guardrail metrics, and holdout design. You build pre/post and diff-in-diff analyses where randomization isn't feasible, and you know how to communicate the difference in confidence between the two
- Data layer ownership — you're the DRI for data quality in your product area: auditing dbt models, writing briefs for analytics engineering, and debugging ambiguous numbers to the source.
- Expert-level SQL — you build complex multi-step queries across large behavioral datasets, optimize for performance, and explain the logic to non-technical stakeholders.
- Statistical reasoning — you understand distributions, confidence intervals, significance testing, and sample size constraints. You design for confounders, know when you have enough signal, and communicate uncertainty in plain language.
- Data modeling literacy — you translate business logic into data requirements, specify the grain a table needs, and have informed opinions on how metrics get structured in dbt.
- Comfort with imperfect data — you make a reasoned call, state your confidence level, and give stakeholders something actionable rather than waiting for clean infrastructure.
- Stakeholder fluency — you work effectively across Product, Design, Engineering, and Product Marketing, and hold your ground respectfully when data contradicts what the team wants to believe.
- AI as an accelerator — you've used LLMs to write SQL, draft frameworks, or structure analysis, and you know where they're reliable and where they need guardrails.
- BI tool experience — Metabase, Looker, Tableau, Sigma, or similar.
- dbt-comfortable (reading models, understanding lineage) with Snowflake or a comparable cloud warehouse.
- Fintech familiarity — attach rate, payment rails, KYC, AP workflows, pre-authorized payments, balance/deposit metrics, churn dynamics — is a meaningful differentiator.
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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