Senior Manager, Data Engineering
- Compensation
- CA$142K–CA$177K / yr
- Location
- Toronto
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data engineering
- Posted
Skills
Must have
- University degree in Computer Science, Engineering, or a related discipline; equivalent experience will be considered.
- 10+ years of progressive experience in Data Engineering and enterprise data platform delivery.
- Experience leading engineering teams and delivering large-scale data solutions in complex enterprise environments.
- Hands-on experience with modern data platforms such as Snowflake and Databricks.
- Experience designing and supporting data lakes, data warehouses, streaming platforms, and modern analytics ecosystems.
- Strong knowledge of cloud platforms including AWS, Azure, or GCP.
- Experience with SQL, NoSQL, and distributed data processing frameworks.
- Proficiency in Python, Scala, Java, or similar programming languages.
- Experience establishing engineering standards, CI/CD practices, automation frameworks, and operational processes.
- Strong understanding of DataOps, MLOps, data quality, security, and governance principles.
- Strong stakeholder management and communication skills with the ability to influence cross-functional teams and executive stakeholders.
- Proven ability to balance strategic planning with execution and operational excellence.
Nice to have
- Experience leading enterprise Snowflake migration and modernization programs.
- Experience supporting machine learning platforms and feature engineering pipelines.
- Experience implementing Infrastructure as Code (IaC) frameworks.
Full description
Your Moneris Career – The Opportunity
As the Senior Manager, Data Engineering, you will lead the data engineering function responsible for delivering reliable, scalable, and secure data platforms that power Moneris' business operations, analytics, and data-driven decision-making.
This role offers the opportunity to shape the future of Moneris' enterprise data ecosystem through platform modernization, cloud transformation, and engineering excellence. You will lead the migration to modern data platforms, including Snowflake, while advancing DataOps and MLOps capabilities that support analytics, machine learning, and business intelligence initiatives. Working closely with architecture, governance, security, and business stakeholders, you will drive delivery of enterprise-scale data solutions, establish engineering best practices, and build a high-performing team focused on innovation, reliability, and measurable business outcomes.
Location: You will be based in our Toronto office, balancing in-office collaboration with remote flexibility.
Reporting Relationship: You will report to the Director, Data Engineering
Posting Type: Replacement Position
Salary Range: The expected base salary range for this role is $142,000 - $177,000
Total compensation may also include variable or discretionary incentive components, including but not limited to bonuses and commissions. Individual job offers are determined by various factors, including experience, education, skills, certifications, and other business needs.
Your Moneris Career – What you’ll do
- Lead and develop a high-performing Data Engineering team through coaching, mentorship, and performance management.
- Define and execute the roadmap for enterprise data platforms, pipelines, and services.
- Lead Snowflake migration initiatives and modernization of legacy enterprise data warehouse (EDW) environments.
- Oversee the design, development, and operation of secure, scalable batch and real-time data solutions.
- Establish engineering standards, development practices, and operational processes to improve platform reliability and performance.
- Partner with Architecture, Governance, Security, and Product teams to implement enterprise data capabilities.
- Enable DataOps and MLOps practices, including feature engineering pipelines and machine learning data workflows.
- Build and enhance CI/CD pipelines, automated testing frameworks, and data quality controls.
- Drive adoption of Infrastructure as Code (IaC), deployment automation, and engineering best practices.