Data Engineer, Analytics | Ingénieur de données, analytique

Role overview

Responsibilities

• Design and implement scalable ETL and ELT pipelines from diverse sources into analytical data stores.
• Develop and maintain logical and physical data models spanning product use cases and analytics domains.
• Define data quality frameworks, validation checks, monitoring, and upstream logging improvements.
• Establish data access, privacy, and security models aligned with privacy requirements and governance standards.
• Build data staging solutions and visualization interfaces for self-service exploration.
• Contribute to semantic services and metadata frameworks to standardize definitions and reduce duplicate representations.
• Write, debug, and optimize complex SQL and programmatic queries on distributed query engines.
• Translate business requirements into robust infrastructure with data science, product, and engineering partners.
• Propose standards and processes to improve data governance and metadata management.
• Improve shared infrastructure through peer guidance, targeted enhancements, and documentation.

Minimum qualifications

• 5+ years of data engineering experience, including production-grade ETL or ELT pipeline design and implementation.
• 5+ years writing and optimizing SQL queries against large-scale distributed data systems.
• Experience designing logical and physical data models for multiple analytical use cases at scale.
• Experience implementing data quality processes, access controls, and privacy-compliant data handling.
• Experience coding in Python, Scala, or a comparable language for maintainable, testable data pipelines.

Preferred qualifications

• Experience with responsible and ethical AI practices, including risk assessment, bias mitigation, and quality reviews.
• Experience improving upstream data quality through application logging design or structured instrumentation.
• Experience building self-service analytics platforms or connected dashboard ecosystems.
• Ongoing AI skills development, such as prompt or context engineering and agent orchestration.
• Experience with shared data infrastructure, semantic layers, or metadata systems at a large organization.
• Familiarity with Spark, Flink, or equivalent stream processing frameworks.
• Experience integrating AI tools to improve workflows and deliver measurable results.

Compensation

• Base compensation is CA$131,000 to CA$181,000 per year, plus bonus, equity, and benefits.
• Individual compensation depends on skills, qualifications, experience, and location.

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