Senior / Staff Data Engineer | AI, Data Platforms & Data Products | Remote Canada

Fully Remote, Canada Competitive Salary + Equity Canadian work authorisation required, no sponsorship available "The best infrastructure isn't inherited. It's imagined, built, tested and made indispensable." This is not a role for someone who simply maintains an existing data platform. We're looking for someone who has built one. We're working with a fast-growing, venture-backed technology company looking for an exceptional Senior or Staff Data Engineer to become one of the foundational members of its data engineering function. This is an opportunity for someone who loves the difficult, high-leverage end of data engineering: building platforms from scratch, making architectural decisions, creating AI-powered data products and owning systems all the way into production. You won't be joining a huge data organisation with layers of specialists around you. You'll work closely with an experienced technical data lead and take significant ownership across the data platform, helping determine what should be built, how it should be built and what will create the greatest impact for the business and its customers. What makes this role different? They don't want a traditional warehouse or ETL engineer. They want a builder. Someone who has taken a problem from zero to one, then helped scale it from one to one hundred. Perhaps you were an early data hire. Perhaps you helped establish a data engineering function. Perhaps you built a new platform inside a scale-up. Or perhaps you created customer-facing data products that became commercially important. Ideally, you can point to systems and say: "I designed that." "I built that from scratch." "Customers use that." "That generated revenue, saved serious money or fundamentally changed how the company operated." That type of ownership matters far more here than having a long list of technologies on your resume. What you'll be building You'll operate across the full data stack, from infrastructure and distributed systems through to AI-powered analytics and customer-facing data products. You'll be:

• Designing and evolving modern cloud data infrastructure
• Building scalable, reliable pipelines and event-driven systems
• Working with technologies including AWS, Snowflake, Databricks and dbt
• Developing AI-enabled and agentic data capabilities
• Building semantic layers and self-service analytics experiences
• Creating customer-facing and embedded data products
• Tackling streaming, governance, metadata and knowledge-management challenges
• Evaluating emerging technologies as the AI and data landscape evolves
• Owning reliability, observability and production performance
• Participating in on-call and taking responsibility for what you build
• Influencing architecture, engineering standards and the future direction of the data function
  
  

Who are we looking for? You'll likely have 5+ years of professional engineering experience, but years alone won't determine whether you're right. What matters is what you've actually built. We'd particularly like to speak with engineers who can demonstrate:

• Genuine zero-to-one data infrastructure or platform experience
• Strong distributed systems or platform engineering fundamentals
• Experience building rather than simply inheriting data systems
• Production ownership, monitoring, incident response and on-call experience
• AWS or equivalent cloud infrastructure experience
• Snowflake, Databricks, Spark, Airflow, dbt or similar technologies
• Streaming or event-driven architecture
• AI/LLM experience within modern data platforms
• Semantic layers, AI data assistants or natural-language analytics
• Self-service analytics platforms
• Customer-facing or embedded analytics
• Data products tied to revenue, product adoption or customer experience
• Measurable commercial or technical impact
• Staff-level architecture and technical leadership
• Experience mentoring engineers or helping grow a data engineering function
• Startup or scale-up experience where ambiguity comes with the territory
  
  

A background spanning software engineering, data engineering, machine learning or quantitative disciplines could be particularly interesting. What this role is NOT This isn't simply about: Maintaining ETL pipelines. Writing SQL all day. Producing dashboards. Building dbt models inside someone else's architecture. Administering an established warehouse. And having "Principal" or "Staff" in your current title won't necessarily make you right for the role. The strongest candidates will show technical ownership, product thinking, AI curiosity, commercial awareness and measurable impact. You should be comfortable independently identifying an important problem, designing the architecture, defending your technical decisions, building the solution and then owning it in production. Why join? Because very few Staff-level roles offer this level of influence. Instead of becoming engineer number 40 in an established data organisation, you'll help shape how an entire modern data capability develops. The company is growing quickly. The engineering problems are complex. Data sits at the heart of the product. AI is becoming increasingly important to the platform. And the work you build won't disappear into the plumbing. The goal is to create data infrastructure and products that engineers, teams and customers actually depend on. Fully Remote, Canada Competitive Salary + Equity You must already have unrestricted Canadian work authorisation. Visa sponsorship or visa transfer is not available.