Senior Data Engineer

Float

Senior Data Engineer owning Snowflake architecture and building ingestion pipelines for a Canadian fintech. Collaborating with cross-functional teams to ensure reliable data flow and access controls.

Posted 7/23/2026full-timeToronto • 🇨🇦 CanadaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Proficient in Python and SQL with extensive experience in building and owning ELT/ETL pipelines at scale. Demonstrates strong capabilities in data observability, data quality frameworks, and managing cloud data platforms like Snowflake.

Highest-signal resume keywords

Python ProficiencySQL ProficiencyELT/ETL Pipeline DevelopmentData Observability ImplementationSnowflake Architecture Management

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLELTETLData ObservabilityData Quality FrameworksCloud Data PlatformsOrchestrationDbtAI Tooling

Soft Skills

Clear CommunicationTrust BuildingStakeholder Engagement

Tools & Technologies

SnowflakeDatabricksAI Tooling

Industry Keywords

DataOpsData ArchitectureCost OptimizationAccess ControlsData ContractsML PipelinesTelemetry Standards

Tech Stack

Tools & technologies

CloudETLPythonRuby on RailsSQL

About the role

Key responsibilities & impact

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 DataOps 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.

Requirements

What you’ll need

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

Benefits

Comp & perks

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