Senior Data Engineer

We’re looking for a Senior Data Engineer to help us scale our data ecosystem as Relay enters an exciting new chapter of growth. This is a unique opportunity to be responsible for key aspects of the evolution of our data and analytics stack as the company grows. You'll have a dedicated interest in ensuring our teams have access to the right data at the right time in order for us to make the best decisions for our customers.

What You'll Be Doing

• Data Modeling & Transformation: Build and maintain robust dbt models in Snowflake that enable reliable, privacy-preserving analysis across Relay's core data

• Platform Scalability: Own and evolve the systems and processes that underpin our data platform as Relay continues to grow

• Data Infrastructure & Tooling: Design and build the systems that help us track core KPIs and ensure all our teams have the timely and accurate data they need to function effectively

• Analytics & Business Intelligence: Partner with stakeholders to build the systems that track core KPIs and ensure every team has timely, accurate data to operate effectively

• Security & Compliance: Work closely with Trust, Security, and Governance teams to keep customer data safe through best-in-class security and compliance practices

What You'll Bring

• You have 3+ years of Data Engineering experience

• You write SQL that goes well beyond SELECT, WHERE and GROUP BY -- think complex window functions, query optimization, and schema design

• You're fluent in Python and know how to write clean, maintainable, production-grade code

• You have hands-on experience with our core stack: dbt for transformations, Dagster for orchestration, Snowflake as our data warehouse, and cloud infrastructure (AWS, GCP, or Azure)

• You have experience with BI/data visualization tools (Looker, Metabase, Mode, etc.) and understand what it takes to support a great analytics layer

• You have a track record of extracting real value from large, complex datasets -- and a genuine curiosity for what the data is telling you

• You can drive work independently, collaborate well with cross-functional stakeholders, and know when to ask for input vs. just move

Bonus Points:

• You have an understanding of applied statistics, are able to create quantitative models, and can clearly communicate findings to a technical and non-technical audience

• You have domain knowledge of Banking or SaaS analytics, and are familiar with concepts like LTV, churn, retention, etc.

• You have experience with streaming or microbatch analytics (Spark Streaming, Flink, Beam, etc.) and design methodologies of persistent event stores (AWS Kinesis, GCP PubSub, Apache Kafka, Apache Pulsar, etc.)

• You’ve joined a company at its early stages and have seen it through scale

The Interview Process

Stage 1: A 30-minute Google Meet video call with a member of the Talent team

Stage 2: A 45-minute technical deep dive via Google Meet with our Engineering Manager, Data

Stage 3: A 60-minute case study presented to a panel of technical stakeholders

Stage 4: A 45-minute in-person interview with a member of the Leadership team

The annual salary range for this role is $144,000 - $176,000 CAD.

For candidates who demonstrate full readiness for the defined scope of the role, the typical starting salary is $160,000 CAD. Offers below this point reflect candidates we believe can grow into the full scope of the role with support and development. Offers above this point reflect impact that meaningfully exceeds the role’s defined expectations or an expanded scope from day one.

• You push relentlessly for reinvention: You're always asking "how can this be better?" -- in your work, in your craft, in yourself. Comfort is a signal to push harder, not coast. You'd rather build something better than defend something familiar.