Staff Analytics Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Analytics Engineer based in Canada.

This is a senior technical role responsible for building and evolving the analytics platform that powers reporting, self-service analytics, and AI-enabled decision-making.
You will shape the architecture, standards, and governance that ensure data remains reliable, discoverable, and trusted as the organization scales.
The role spans data ingestion, integration, modeling, semantic layers, observability, quality, testing, and CI/CD.
You will work closely with stakeholders across Marketing, Sales, Support, Product, and other business functions to turn complex data needs into scalable solutions.
As a technical leader, you will influence engineering practices, review critical data products, and mentor Analytics Engineers through design and code reviews.
The environment is fast-moving and collaborative, with a strong focus on automation, self-service, AI-augmented workflows, and continuous improvement.
This is an opportunity to build foundational systems that enable teams across the business to make better decisions with confidence.

• Build, operate, and continuously improve the core BI and analytics platform supporting reporting, analytics, self-service capabilities, and AI use cases.
• Own data ingestion, integration, and analytics platform architecture, making sound technical trade-offs as systems and business requirements evolve.
• Design and maintain enterprise data models, reusable business logic, and semantic-layer definitions that provide a consistent foundation for trusted analytics.
• Establish and enforce standards for data quality, testing, governance, observability, documentation, and CI/CD across the analytics engineering environment.
• Monitor platform health, identify pipeline failures and recurring reliability issues, and implement scalable solutions that improve system stability and performance.
• Review and certify data pipelines, data models, semantic-layer definitions, and data products before they are made available to business users.
• Enable self-service analytics through governed datasets, reusable logic, and clearly defined and trusted metrics.
• Explore and implement AI-enabled approaches that improve analytics engineering workflows, including data modeling, testing, documentation, and code development.
• Mentor Analytics Engineers through code reviews, architecture and design reviews, technical guidance, and engineering best practices.

• 6+ years of professional experience in analytics engineering or a similarly technical data analytics role, preferably within SaaS or high-tech environments.
• Expert-level SQL skills and deep experience with data modeling, including the ability to write performant queries and evaluate architectural trade-offs in database design.
• 6+ years of hands‑on experience building, deploying, and maintaining modular production data pipelines using dbt.
• Strong experience with Git and production development workflows, including branching, merging, pull requests, code reviews, and automated data-quality testing.
• Proven experience designing schemas and optimizing query performance on modern cloud data platforms such as Snowflake, Redshift, or Big Query.
• Experience implementing and operating data observability platforms and monitoring solutions.
• Demonstrated experience designing semantic models and enabling self-service analytics through governed datasets, reusable business logic, and trusted metric definitions.
• Experience applying AI tools to improve analytics engineering workflows, including accelerating modeling, testing, documentation, and code development.
• Strong engineering mindset with excellent problem‑solving skills and the ability to understand complex systems and make thoughtful architectural decisions.
• Strong communication and collaboration skills, with the ability to work effectively with both technical teams and business stakeholders.
• A proactive, adaptable approach and willingness to work through ambiguity, learn continuously, and improve existing systems and processes.

• Competit…