Senior Data Engineer

Float

12d•Hybrid

About The Position

Float is Canada’s intelligent financial operating system, combining modern financial services and software to help businesses spend, save, and grow. Trusted by more than 7,500 Canadian companies, Float provides high-limit corporate cards, automated expense management, next-day bill payments, high-yield accounts, and industry-leading support, all built in Canada, for Canada. Float recently announced our $85 million Series C, and is backed by world-class investors, including Inovia Capital, Growth Equity at Goldman Sachs Alternatives, OMERS Ventures, and Silicon Valley Bank. Our team is a collection of ambitious, collaborative and mission-driven people from all walks of life but with one goal: helping Canadian companies not just survive but thrive. And we’re looking for bold innovators to help shape the future of business finance in Canada. We use technology, including artificial intelligence (AI), to support parts of our hiring process. This may include AI-assisted scheduling and candidate communications, and AI-generated interview notes, guides or summaries to help our team focus on the conversation. All hiring decisions are made by our hiring team. This is Float's first dedicated data engineering hire. You'll own the Snowflake architecture and ingestion layer end-to-end — the plumbing that carries data from banking partners, card processors, CRM, marketing platforms, and product systems into one clean, reliable, well-governed place. You'll work closely with our Analytics Engineer, who owns the dbt and transformation layer, and be the infrastructure backbone our Data Scientists and growing analytics team depend on. Together, you and the Analytics Engineer form the core of a data platform the rest of Float can trust and build on. We're at an inflection point. Growing transaction volumes and an expanding product surface mean there's real work to do — and you'll have the scope and ownership to do it properly.

Requirements

Genuine proficiency in Python and SQL — not just functional, but strong
Hands-on experience building and owning ELT/ETL pipelines at scale
Deep experience with a modern cloud data platform (Snowflake, Databricks, or equivalent), including architecture, cost optimization, and access controls
Real orchestration experience and a clear point of view on how to use it well
dbt fluency — you won't own the transformation layer, but you'll work seamlessly with the person who does
Practical experience implementing data observability, data quality frameworks, and data contracts — not just familiarity with the concepts, but having actually put them in place
A track record of looking at a messy data environment and developing a clear, pragmatic point of view on what good looks like — and how to get there without over-engineering
You've built something from scratch in a high-ownership environment
You communicate clearly with non-technical stakeholders, earn trust across a business, and hold your technical opinions strongly but loosely
You use AI tooling deliberately and daily — for coding, for monitoring, and for triage — and you have a clear point of view on where it genuinely accelerates your work and where it doesn't

Nice To Haves

Experience with data governance and compliance in a regulated environment
Fintech or payments background — understanding how transaction, card, and banking data behaves

Responsibilities

Own Float's Snowflake architecture end-to-end — organization, cost efficiency, and access controls
Design and build a real orchestration layer for our ingestion pipelines, replacing ad hoc processes
Stand up observability and alerting so pipeline failures and data staleness are caught immediately, not downstream
Introduce data contracts between producer and consumer systems, so upstream changes (new payment rails, new banking products, vendor field changes) stop silently breaking things
Own the data reliability layer — establish CI/CD pipelines for data code, build data quality checks directly into pipelines, and orchestrate jobs that live outside of ingestion and dbt (ML pipelines, data quality runs, operational workflows), so everything is tested, automated, and deployed consistently
Partner closely with our Analytics Engineer to keep the ingestion → transformation handoff clean and dependable — and collaborate on lineage tracking and metadata tooling so the broader team has visibility into where data comes from and how it flows
Develop, document and socialize a target-state data architecture and phased migration roadmap, in partnership with data and engineering leadership.
Build and maintain the data pipelines that power ML model training and inference — ensuring the data science team has reliable, well-structured feature data for ML use cases
Leverage AI tooling across the full engineering lifecycle — from writing and reviewing pipeline code to accelerating observability, anomaly detection, and incident triage
Partner with product and infrastructure engineering to maintain consistent telemetry standards across Float's codebases, ensuring product events flow reliably into the data platform.