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. Accountabilities 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. Requirements 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 BigQuery. 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. Benefits Competitive Canadian salary range of CAD $152,000-$237,500, with an accomplished-stage reference point of approximately CAD $180,500. Equity participation through a company stock option plan. Significant opportunity for compensation growth as impact, expertise, and organizational contribution increase. Remote work environment for employees based in Canada. Opportunity to work within a high-growth SaaS organization with a distributed international team. Meaningful ownership over the architecture, governance, and reliability of a critical analytics platform. Exposure to modern cloud data platforms, dbt, data observability, semantic modeling, and AI-augmented engineering practices. Strong opportunities for technical leadership, mentorship, and professional growth. Collaborative culture focused on transparency, continuous learning, customer impact, and solving complex problems together. Benefits package and additional employee programs available according to Canadian employment terms. How Jobgether Works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Lead the architecture and evolution of the analytics platform to support reporting, self-service analytics, and AI-driven decision-making. Oversee data ingestion, modeling, and governance while mentoring other analytics engineers through code and design reviews.

Requires over 6 years of experience in analytics engineering, specifically with expert-level SQL and production-grade dbt pipelines. Must have proven experience with cloud data platforms like Snowflake or BigQuery and a strong engineering mindset for scalable system design.

• Stock Options • Remote Work • Professional Growth Opportunities • Employee Programs

• SQLPreferred
• GitPreferred
• CI/CDPreferred
• Problem solvingPreferred

Identified from the job description. Confirm important requirements above.

• SQL
• dbt
• Data Modeling
• Git
• Snowflake
• Redshift
• BigQuery
• Data Observability
• Semantic Modeling
• CI/CD
• AI-Augmented Workflows
• Architecture Design
• Data Governance
• Technical Leadership
• Mentorship
• Problem Solving

• Data & Analytics
• Technology
• Engineering
• Software
• Management & Leadership

Minimum experience

5+ years

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level

Application method

Direct apply is available