Senior Data Engineer

Float•Toronto, ON

1d•Hybrid

About The Position

Float is seeking its first dedicated Data Engineer to own the Snowflake architecture and ingestion layer end-to-end. This role is crucial for building a clean, reliable, and well-governed data platform that supports banking partners, card processors, CRM, marketing platforms, and product systems. The Senior Data Engineer will collaborate closely with the Analytics Engineer, forming the core of the data platform that Data Scientists and the analytics team depend on. The company is at an inflection point with growing transaction volumes and an expanding product surface, offering significant scope and ownership for this role.

Requirements

Genuine proficiency in Python and SQL.
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.
Practical experience implementing data observability, data quality frameworks, and data contracts.
A track record of developing a clear, pragmatic point of view on data environments and how to improve them without over-engineering.
Experience building something from scratch in a high-ownership environment.
Clear communication with non-technical stakeholders, ability to earn trust across a business, and ability to hold technical opinions strongly but loosely.
Deliberate and daily use of AI tooling for coding, monitoring, and triage, with a clear point of view on its effectiveness.

Nice To Haves

Experience with data governance and compliance in a regulated environment.
Fintech or payments background, including understanding of transaction, card, and banking data behavior.

Responsibilities

Own Float's Snowflake architecture end-to-end, including organization, cost efficiency, and access controls.
Design and build a real orchestration layer for ingestion pipelines, replacing ad hoc processes.
Implement observability and alerting for pipeline failures and data staleness.
Introduce data contracts between producer and consumer systems to prevent silent failures.
Own the DataOps layer, establishing CI/CD pipelines for data code, building data quality checks, and orchestrating jobs outside of ingestion and dbt.
Partner with the Analytics Engineer to ensure a clean and dependable ingestion-transformation handoff, and collaborate on lineage tracking and metadata tooling.
Develop, document, and socialize a target-state data architecture and migration roadmap with data and engineering leadership.
Build and maintain data pipelines for ML model training and inference, providing reliable feature data to the data science team.
Leverage AI tooling across the engineering lifecycle for coding, monitoring, and incident triage.
Partner with product and infrastructure engineering to maintain consistent telemetry standards and ensure reliable product event flow into the data platform.