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

Accountabilities: Design, build, and evolve modern cloud-based data infrastructure and scalable data platforms from the ground up. Develop reliable data pipelines, distributed systems, and event-driven architectures that support business-critical applications and products. Work across technologies such as AWS, Snowflake, Databricks, Spark, Airflow, and dbt to create robust and maintainable data solutions. Develop AI-enabled data capabilities, including LLM-powered workflows, agentic data solutions, semantic layers, and natural-language analytics experiences. Build self-service analytics platforms and customer-facing or embedded data products that improve product adoption, customer experience, and commercial performance. Address complex challenges involving data streaming, governance, metadata management, and knowledge systems. Evaluate emerging technologies and identify opportunities to improve the data platform as the AI and data ecosystem evolves. Own systems throughout their lifecycle, including deployment, production reliability, observability, performance monitoring, incident response, and on-call support. Define architectural direction, establish engineering standards, and make informed technical decisions that support long-term scalability. Collaborate with technical leadership and cross-functional teams to identify high-impact problems, prioritize solutions, and connect engineering investments to business outcomes. Mentor other engineers, share technical expertise, and help establish and grow the data engineering function. Requirements At least 5 years of professional engineering experience, with demonstrated achievements and technical ownership valued more highly than tenure alone. Proven experience building data infrastructure, platforms, or data products from scratch, ideally as an early engineering hire or within a startup or scale-up environment. Strong foundations in distributed systems, platform engineering, software engineering, or modern data engineering. Hands-on experience with cloud infrastructure, particularly AWS or an equivalent cloud platform. Familiarity with modern data technologies such as Snowflake, Databricks, Spark, Airflow, and dbt. Experience designing streaming or event-driven architectures and building scalable, reliable production systems. Demonstrated ownership of production environments, including monitoring, observability, troubleshooting, incident response, and on-call responsibilities. Experience with AI and large language models in data platforms, including AI-assisted analytics, semantic layers, AI data assistants, or natural-language interfaces, is highly desirable. Experience developing self-service analytics capabilities, embedded analytics, or customer-facing data products. A track record of delivering measurable technical or commercial impact, such as generating revenue, reducing costs, improving customer experience, or increasing operational efficiency. For Staff-level candidates, the ability to lead architecture decisions, influence technical direction, establish engineering practices, and mentor other engineers. Strong product thinking, commercial awareness, analytical problem-solving skills, and the ability to communicate and justify complex technical decisions. Comfortable operating autonomously in ambiguous environments, identifying important problems, designing solutions, and taking responsibility for outcomes from conception through production. A background in software engineering, data engineering, machine learning, or quantitative disciplines may be particularly relevant. Work authorization: Must already have unrestricted authorization to work in Canada. Visa sponsorship and visa transfer are not available. Benefits Competitive salary aligned with experience and seniority; the specific salary range has not been disclosed. Equity participation , offering an opportunity to share in the company’s growth. Fully remote work for eligible candidates based in Canada. Significant ownership and influence over data platform architecture, engineering strategy, and AI-powered product development. The opportunity to help establish and shape a growing data engineering function rather than simply maintain an existing platform. Exposure to complex technical challenges spanning cloud infrastructure, distributed systems, AI, analytics, and customer-facing data products. Direct collaboration with experienced technical leadership and the opportunity to influence high-impact business decisions. An environment that values autonomy, experimentation, innovation, and measurable outcomes. Healthcare coverage, paid leave, retirement benefits, and other specific employment perks were not detailed in the provided job description.