Data Analyst, Product (Banking and Payments)

Float•Toronto, ON

12d•Hybrid

About The Position

Float is building the financial operating system for Canadian businesses — corporate cards, business bank accounts, bill payments, all in one place. Reporting to the Head of Data, this role is embedded inside the product teams behind two of those core pillars: banking infrastructure and payments. Both are high-complexity, high-stakes areas that generate a dense mix of financial, behavioral, and operational data. This role owns that data layer. You're building cohorts before anyone asks, designing measurement frameworks before features go to engineering, and debugging numbers to the source — pipeline issue, definition mismatch, or real product signal. You're also someone who uses AI to move faster without losing rigour. Getting useful outputs from a model means knowing the business and data well enough to give it the right context — that's part of the job too.

Requirements

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.
2–4+ years in product analytics, ideally embedded in a product team at a high-growth fintech or SaaS company.
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.

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.