Relay is a digital banking platform that gives self-made business owners the tools and know-how to be great with money—bringing clarity, confidence, and control to every dollar earned, so they can turn hard work into lasting success. We do this by replacing financial guesswork with real visibility, transforming cash flow from a constant source of stress into a clear signal owners can use to run stronger, more resilient businesses. Relay's data engineering team has tripled in size, from 4 to 12, and is now splitting into two teams.

Analytics Data

Engineering works close to the business, building the models, pipelines and dashboards that teams use to run the company. Data Platform , the team you'll lead, owns the foundation underneath: infrastructure, cost, real-time data, observability, and the AI capabilities the rest of Relay builds on. The team's north star is platform health .

Lead and grow a senior team of data engineers. With an experienced team in place, your role is to provide direction, challenge thinking, and help the team make decisions and move work forward. Get data into stakeholders' hands faster.

You'll define and drive Relay's approach to real-time data and platform enablement, including streaming (Kafka) and a Snowflake MCP server that opens our data to AI tooling. You’ll help the team prioritize upgrades, migrations and refactors by linking technical investments to business value, whether that’s driving revenue, improving efficiency or reducing risk. Balance speed against technical debt, in both directions.

Sometimes you'll persuade the team to ship with some debt because the business value is there. That includes standups, sprint and roadmap planning, architectural decisions, code review, and the requests that come in from across Relay. You have at least two years of experience managing engineers, including senior team members with robust technical perspectives and the expertise to back them up.

You have deep, hands-on production experience with Snowflake and dbt , including building data models.



You've owned a production data platform, and ideally you've led meaningful improvements to one, such as cutting cost, improving reliability or adding new capability. You ask why before you ask how long, and you can find a path that serves both the engineer's concern and the business deadline.

You're highly fluent with AI tools, both in how you work and in how you'd guide a team building AI-enabled data capabilities.

Bonus

Points AWS or GCP Dagster or another data orchestrator Kafka or other real-time data infrastructure Stage 1: A 45-minute Google Meet video call with a member of our Talent team Stage 2: A 60-minute Google Meet video call with the Hiring Manager Stage 3: A take-home case study followed by a 60-minute Google Meet video call with our Engineering team Stage 4: A 45-minute Google Meet video call with a member of our leadership team The annual salary range for this role is 207,000 CAD to 253,000 CAD. For more information on our compensation philosophy and perks and benefits, visit our Candidate Hub. If you’re someone who takes initiative, is comfortable taking risks, and seeks input when needed, you’ll find the freedom here empowering.

You build with AI, not just use it: You're actively embedding AI into how you work, pushing what's possible, and bringing your team along with you. You’re comfortable building from scratch, iterating as you go, and collaborating to shape the best path forward. Relay exists to help them thrive -- and that mission has to resonate with you.

We welcome diverse backgrounds and people who think critically, challenge the status quo, and want to help small businesses thrive. If you require accommodations at any stage of the hiring process, please reach out to your Talent Partner. Relay’s recruitment team only contacts candidates and corresponds from official corporate email addresses ending in @relayfi.We will never ask for sensitive personal data (such as bank account details, credit card numbers, or social security/insurance numbers) during the initial application or interview stages. #

📌 Data Engineering Manager, Analytics (Toronto)
🏢 Relay
📍 Toronto