MLOps / Data Engineer

Jobgether; Quebec; Hybrid; CAD 100,000 - 125,000; Full time

Benefits offered by this job

Remote work from Canada

4 weeks vacation + 5 personal days

Health insurance

Telemedicine

EAP

RRSP with employer matching

Internet reimbursement

Job summary

Jobgether, on behalf of a partner company, is seeking an MLOps / Data Engineer based in Canada. You will bridge data science and production, building scalable ML pipelines and data infrastructure for a global digital audio advertising ecosystem.

This fully remote role requires strong Python, Spark, Databricks, and CI/CD skills, with collaboration across global teams. Ontario or Quebec candidates are eligible, with competitive CAD salary and comprehensive benefits.

Qualifications

• Proven experience in Data Engineering, MLOps, or DevOps with automation.
• Strong Python and Spark expertise; Scala is a plus.
• Databricks proficiency including Delta Lake and streaming.
• CI/CD implementation across cloud-based ML workloads.
• Experience with AWS, Azure, or GCP.
• Kubernetes or OpenShift in production environments.
• Ability to design scalable data pipelines.

Responsibilities

• Design and maintain MLOps pipelines with CI/CD.
• Develop scalable data processing using Spark and PySpark.
• Manage Databricks environments and cost controls.
• Productionize ML models via APIs and batch systems.
• Implement testing, monitoring, and alerting for data pipelines.
• Promote best practices in versioning and reproducibility.
• Collaborate with data scientists and engineers.
• Support advertising infrastructure with data feeds.

Skills

Python programming

Apache Spark

Databricks

CI/CD

Cloud platforms

Kubernetes

Data engineering

MLOps/DevOps

English proficiency

French asset

Tools

GitHub Actions

Azure DevOps

Jenkins

GitLab CI

ArgoCD

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a MLOps / Data Engineer based in Canada.

As an MLOps / Data Engineer, you will play a key role in bridging data science and production engineering, transforming machine learning models into reliable, scalable, and high-performance solutions. You will contribute to the development of advanced data infrastructure supporting the global digital audio advertising ecosystem. Working with large-scale listener datasets, you will design automated pipelines, optimize distributed data processing, and productionize machine learning workflows. Your work will help turn audience intelligence into structured, actionable signals for modern advertising platforms and emerging AI-driven buying systems. You will collaborate closely with data scientists and engineering teams in a technically advanced, cross-functional environment. This fully remote opportunity offers the chance to shape the future of data-driven advertising technologies.

Accountabilities

• Design and maintain MLOps pipelines: Build, implement, and manage automated CI/CD pipelines for machine learning workflows using tools such as GitHub Actions, Azure DevOps, Jenkins, GitLab CI, or ArgoCD.
• Develop scalable data processing solutions: Build and optimize distributed data pipelines using Apache Spark, PySpark, and Scala to process large-scale listener datasets efficiently.
• Manage Databricks environments: Configure and maintain Databricks infrastructure, optimize cluster performance, manage job scheduling, and control operational costs.
• Productionize machine learning models: Collaborate with data scientists to deploy ML models into production through scalable APIs and batch-processing systems that generate actionable audience intelligence.
• Ensure reliability and performance: Implement automated testing, monitoring, and alerting mechanisms to maintain the reliability, reproducibility, and performance of data and machine learning pipelines.
• Establish engineering best practices: Promote effective version control, model registry management, environment reproducibility, and automation standards.
• Support next-generation advertising infrastructure: Help evolve listener data systems toward structured, continuously available, machine-readable data feeds that can be accessed and used by autonomous advertising platforms.
• Drive technical improvements: Evaluate architectural decisions and optimize infrastructure across performance, scalability, cost efficiency, and long-term maintainability.

Requirements

• Professional experience: Proven experience in Data Engineering, MLOps, or DevOps roles, particularly involving automation, distributed systems, and scalable infrastructure.
• Programming expertise: Strong Python programming skills and Hands-on experience with Apache Spark. Knowledge of Scala is a significant advantage.
• Databricks proficiency: Advanced experience with Databricks, including Delta Lake, structured streaming, and feature engineering.
• CI/CD knowledge: Strong understanding of continuous integration and continuous delivery principles, with practical experience using tools such as GitHub Actions, Jenkins, Azure DevOps, GitLab CI, or ArgoCD.
• Cloud expertise: Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform for data engineering and machine learning workloads.
• Containerization and orchestration: Experience working with Kubernetes or OpenShift environments, including deploying, scaling, and ensuring fault tolerance for data and ML workloads.
• Architectural capabilities: Strong technical judgment and the ability to assess trade-offs involving infrastructure costs, performance, scalability, and maintainability.
• Analytical and problem-solving skills: Ability to investigate complex technical challenges, develop practical solutions, and continuously improve engineering processes.
• Collaboration: Comfortable working across multidisciplinary teams, particularly with data scientists and software engineers.
• Communication: Advanced English proficiency is required to collaborate with international teams and to access technical documentation and resources. French proficiency is considered an asset.
• Industry knowledge: Familiarity with IAB data standards, programmatic advertising infrastructure, or AdTech data pipelines is a strong advantage.
• Location eligibility: Candidates must be based in Ontario or Quebec, Canada.

Benefits

• Competitive compensation: Annual salary budget ranging from CAD 100,000 to CAD 125,000.
• Fully remote work: Work remotely from Canada.
• Generous paid time off: Four weeks of annual vacation plus five additional paid personal days.
• Comprehensive health coverage: Group insurance benefits starting on your first day.
• Telemedicine services: Access to convenient virtual healthcare services.
• Employee Assistance Program (EAP): Resources and support for personal and professional well-being.
• Retirement savings: Collective Registered Retirement Savings Plan (RRSP) with employer matching contributions.
• Remote work support: Internet expense reimbursement.
• Inclusive workplace: Join a diverse and collaborative environment that values respect, equity, and professional development.