Canada
The Data Product Analyst serves as a critical bridge between business stakeholders and data engineering for the international data platform. This role combines strong SQL and technical depth with core business analysis skills to ensure that data is trustworthy, accurately specified, and effectively leveraged to inform decision making. The Data Product Analyst owns the path from business request to trusted data in production: eliciting complete requirements, following the work through build and release, and monitoring the data once it is live. Over time, the role becomes the subject matter expert for international data.
Bachelor's degree in information systems, computer science, data analytics, or a related field, or equivalent experience.
5+ years of experience in data analysis, product analytics, analytics engineering, or similar roles, with extensive hands-on SQL experience.
Proven ability to work effectively in cross functional environments and support the delivery of data products that serve multiple stakeholders.
Demonstrated record of ownership: following requests through delivery and production and resolving issues across business and engineering teams.
Advanced SQL and analytical toolkit (with Python proficiency) used to explore, transform, and validate complex datasets across multiple product disciplines, including tracing data lineage and verifying data integrity without engineering dependencies.
Solid technical background in data modeling, ETL/ELT pipelines, and cloud data platforms (e.g., AWS), with enough depth to understand how data products are built and to partner credibly with engineers.
Skilled at requirements elicitation and translating business needs into technical specifications.
Skilled in crafting user stories, acceptance criteria, and high-quality product and data documentation to support scalable, repeatable workflows.
Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use and the ability to adapt analytical and stewardship skills across a wide range of use cases.
Comfortable connecting data across multiple systems and enriching datasets to enable new insights for business stakeholders.
Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders.
Collaborative mindset with the ability to influence without authority and drive alignment across cross functional teams.
Thrives in ambiguity by asking clarifying questions, exploring new data sources, and uncovering insights that drive team action and improve data quality.
Strong critical thinking and structured problem-solving, especially when interpreting incomplete, messy, or evolving datasets.
Requirements
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