MLOps, Data Engineer

Triton Digital

. Design, implement, and maintain CI/CD pipelines for machine learning workflows using tools like GitHub Actions, Azure DevOps, or Jenkins .

Posted 11/2/2026full-timeRemote • CanadaMid-LevelSenior💰 CA$100,000 - CA$125,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Engineering and MLOps, focusing on automation and scalability through CI/CD pipelines, data processing, and machine learning workflows. Proficient in deploying and managing Databricks environments and collaborating with cross-functional teams to integrate ML models into scalable systems.

Highest-signal resume keywords

Data EngineeringMLOpsCI/CD PrinciplesApache SparkDatabricks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonApache SparkScalaDatabricksCI/CDGitHub ActionsJenkinsAzure DevOpsKubernetesOpenShift

Soft Skills

Problem-Solving MindsetCollaborationArchitectural Mindset

Tools & Technologies

GitLab CIArgoCDAWSAzureGCP

Industry Keywords

IAB Data StandardsProgrammatic AdvertisingAdTechData Pipelines

Tech Stack

Tools & technologies

ApacheAWSAzureCloudGoogle Cloud PlatformJenkinsKubernetesOpenShiftPySparkPythonScalaSpark

About the role

Key responsibilities & impact

Design, implement, and maintain CI/CD pipelines for machine learning workflows using tools like GitHub Actions, Azure DevOps, or Jenkins
Build and optimize data processing pipelines in Apache Spark (PySpark and Scala) for large-scale, distributed listener datasets
Deploy and manage Databricks environments, ensuring efficient cluster usage, job scheduling, and cost optimization
Collaborate with data scientists to productionize ML models, integrating them into scalable APIs or batch processing systems that feed real-time, machine-readable audience signals
Implement automated testing, monitoring, and alerting for ML pipelines to ensure reliability and reproducibility
Champion best practices in version control, model registry management, and environment reproducibility
Help evolve listener data infrastructure toward agent-compatible supply with live, structured, queryable data feeds

Requirements

What you’ll need

Proven experience in Data Engineering, MLOps, and DevOps roles with a focus on automation and scalability
Strong programming skills in Python, with hands-on experience in Apache Spark
Scala is a huge plus
Advanced expertise in Databricks, including Delta Lake, structured streaming, feature engineering
Solid understanding of CI/CD principles and tools (e.g., GitHub Actions, Jenkins, Azure DevOps, GitLab CI, ArgoCD)
Familiarity with cloud platforms (AWS, Azure, or GCP) for data and ML workloads
A problem-solving mindset and the ability to work closely with cross-functional teams
Strong architectural mindset, capable of evaluating trade-offs across cost, performance, scalability, and maintainability when selecting tools and designing systems
Experience working with containerized and orchestrated environments (Kubernetes / OpenShift), including deployment, scaling, and fault tolerance of data and ML workloads
Advanced English required
French is an asset
Familiarity with IAB data standards, programmatic advertising infrastructure, or AdTech data pipelines is a strong asset

Benefits

Comp & perks

Fully remote position (must be based in ONTARIO or QUEBEC)
4 weeks of vacation + 5 paid personal days annually
Group insurance programs as of your first day, including access to telemedicine and an EAP
Collective RRSP with matching contribution
Internet reimbursement