As a Senior Data & Analytics Engineer, you will play a key role in building and optimizing our marketing data pipelines and solutions. You will collaborate with passionate colleagues, work on exciting projects, and have the opportunity to grow your skills while making a meaningful impact on the business

Responsibilities

• Design, implement, and maintain robust, scalable ELT data pipelines to support marketing analytics, ensuring efficient integration with CI/CD processes.
• Develop automated testing and deployment workflows while maintaining strong version control practices using Azure Repos
• Work closely with marketing teams to understand data needs and deliver high-quality solutions
• Collaborate closely with Agile squads to drive iterative development, ensure efficient workflows, and foster a culture of continuous improvement and innovation
• Build data integrations from multiple sources, including CRM, digital, and social platforms
• Design, implement and optimize data models with a medallion architecture using Star Schema and Snowflake techniques to enhance query performance and support analytical workloads
• Ensure data quality, consistency, and reliability across all marketing datasets
• Collaborate with analysts and data scientists to deliver clean, usable data for reporting and advanced analytics
• Help establish best practices and contribute to a positive, inclusive, learning-focused team culture
• Document technical processes clearly to ensure easy future maintenance
• Stay up to date with new tools, technologies, and approaches in data engineering and marketing analytics

Qualifications and Skills

• Significant experience with: Snowflake DBT Python Dagster, Airflow, or similar orchestrating tool
• Knowledge of additional technologies is a plus: Azure Microsoft SQL Server Power BI
• Strong proficiency in: SQL Python
• Familiarity with additional languages is a plus: Javascript / Node.js Java
• Experience working with marketing or customer data is a plus
• Understanding of CRM systems, digital marketing platforms, or campaign analytics
• Experience validating data quality and performing data profiling
• Comfortable working in Agile teams and with DevOps practices
• Strong interpersonal and communication skills, in English and French
• Passion for continuous learning and openness to new ideas and technologies
• A collaborative and supportive working style, with an openness to sharing knowledge
• Commitment to fostering an inclusive and respectful team environment
• Experience with Adobe Campaign, Google Analytics, Salesforce, or marketing automation platforms, an important plus
• Power BI or marketing dashboard development experience
• Familiarity with AI/ML applied to marketing (e.g., customer segmentation, churn prediction)
• Certifications in Snowflake, DBT, Azure, or related fields

Responsibilities

• Design, implement, and maintain robust, scalable ELT data pipelines to support marketing analytics, ensuring efficient integration with CI/CD processes.
• Develop automated testing and deployment workflows while maintaining strong version control practices using Azure Repos
• Work closely with marketing teams to understand data needs and deliver high-quality solutions
• Collaborate closely with Agile squads to drive iterative development, ensure efficient workflows, and foster a culture of continuous improvement and innovation
• Build data integrations from multiple sources, including CRM, digital, and social platforms
• Design, implement and optimize data models with a medallion architecture using Star Schema and Snowflake techniques to enhance query performance and support analytical workloads
• Ensure data quality, consistency, and reliability across all marketing datasets
• Collaborate with analysts and data scientists to deliver clean, usable data for reporting and advanced analytics
• Help establish best practices and contribute to a positive, inclusive, learning-focused team culture
• Document technical processes clearly to ensure easy future maintenance
• Stay up to date with new tools, technologies, and approaches in data engineering and marketing analytic