Manager, Data Engineering (Analytics)

Brother Canada•Dollard-Des Ormeaux, QC

26d•Hybrid

About The Position

The Manager, Data Engineering is responsible for leading the development and optimization of the organization's data infrastructure to enable advanced analytics and data-driven decision-making. This role oversees the design, integration, and governance of data pipelines and platforms, ensuring they are scalable, reliable, and aligned with business objectives. Collaborating with members of the Analytics & Insights (A&I) team, IT, and other stakeholders, this role ensures that data engineering practices drive measurable business outcomes. By fostering a high-performing team and advancing organizational analytics capabilities, this role plays a key part in transforming data into actionable insights and contributes to the team’s mission to transform the decision-making culture at Brother through data & science. The Manager, Data Engineering, will be responsible for establishing robust and secure data infrastructure, delivering high-quality, accessible, and reliable data for analytics and decision-making, and enhancing the performance of the data engineering team all while advancing the organizational maturity of the data engineering team and practices.

Requirements

Bachelor’s degree in computer science, Data Engineering, IT, or related field
8+ years of data engineering and analytics experience
3+ years managing teams and complex data engineering projects
Experience with current data platforms (e.g., Azure, AWS, Snowflake) and ETL/ELT tools (e.g., Alteryx, Databricks)
Strong understanding of data governance, security, and compliance best practices
Experience with designing data models, warehouses, and pipelines to support advanced analytics
Experience with visualization tools (Tableau, Power BI)
Strong business acumen and ability to align technical efforts with strategic goals
Demonstrated ability to manage technical teams, foster collaboration, and deliver results in a dynamic environment
Ability to communicate technical content and analytical insights/complex findings in a clear and concise manner to multiple audiences, including senior management
Ability to multi-task and manage simultaneous projects
Strong analytical and problem-solving skills, with a focus on delivering actionable solutions
Excellent communication and interpersonal skills to collaborate with diverse teams effectively

Nice To Haves

Master’s degree or MBA
Familiarity with machine learning frameworks and concepts

Responsibilities

Lead the Development of Data Infrastructure: Be responsible for the design and implementation of scalable, high-performing data pipelines and platforms, ensuring seamless integration of data from various sources.
Establish Governance Standards: Implement data governance policies, ensuring compliance, security, and data quality standards are consistently met.
Drive Innovation in Analytics Enablement: Partner with Data Scientists to design machine learning pipelines, enabling advanced analytics through optimized data preparation and feature engineering workflows.
Design and Oversee Data Integration Processes: Direct the integration of data from diverse systems using industry-leading tools such as Databricks, Alteryx, and Azure, ensuring data harmonization for analytical use.
Optimize Data Performance: Guide the optimization of data structures and workflows to enhance query performance, processing efficiency, and scalability.
Advance Organizational Capabilities: Stay abreast of emerging technologies, evaluate their relevance, and guide their adoption to improve data engineering practices and analytics outcomes.
Champion Data Quality: Oversee the implementation of robust data validation and monitoring processes to ensure the accuracy, consistency, and reliability of data assets.
Support Organizational Strategy: Contribute to the design and implementation of analytics practices, data engineering frameworks, and management methodologies that support the organization’s strategic objectives.
Establish Standard Methodologies: Guide the standardization and adoption of standard processes in data engineering, analytics methodologies, project execution, and governance across the team and organization.
Team Leadership and Development: Build and manage a high performing team by providing mentorship, coaching, and development opportunities to enhance technical and professional skills. Foster a collaborative, growth-oriented environment and ensure the team has the necessary resources to excel.
Performance Management: Set clear objectives, manage workloads, provide regular feedback, and conduct annual performance reviews. Address performance challenges in partnership with HR to build engagement and accountability.
Competency Development: Support the development of technical and analytical skills within the team, identifying areas for growth and ensuring continuous learning opportunities.
Coaching and Mentoring: Provide regular coaching to team members to guide their professional development and improve their expertise in data engineering and analytics methodologies.
Manage Operations: Supervise daily team operations, ensuring projects are completed on time and aligned with organizational goals and priorities.
Communication: Ensure effective communication within the team and with other departments, sharing relevant project updates, challenges, and outcomes to maintain alignment.
Team Branding: Advocate for and communicate the team’s achievements to highlight their value in driving business outcomes and position them as trusted business partners and subject matter experts.
Drive Continuous Improvement: Foster a culture of innovation by identifying and automating inefficient processes, optimizing workflows, and applying analytics to improve operations.
Stakeholder Engagement: Act as a liaison between the team, Analytics & Insights, IT, and business stakeholders to align technical efforts with organizational priorities and drive data-driven transformation goals.
Collaboration: Partner with data scientists, analysts, and business stakeholders to understand data needs and deliver tailored solution.