MLOps / Data Engineer

Jobgether; United States; Remote; USD 71,000 - 89,000; Full time

Benefits offered by this job

Fully remote work

Four weeks vacation

Health coverage

Telemedicine services

EAP

RRSP with employer matching

Internet expense reimbursement

Inclusive workplace

Job summary

Jobgether is seeking an experienced Data Engineer / MLOps Engineer to design and maintain scalable ML pipelines in a fully remote setup across Canada and support advertising data infrastructure. The role requires collaboration with data scientists and software engineers to productionize models and ensure reliable pipelines.

You will work with Python, Spark, and Databricks, implementing CI/CD across ML workflows and optimizing cloud resources.

Qualifications

• Experience in Data Engineering, MLOps, or DevOps with automation and distributed systems.
• Strong Python and Spark; Scala is a plus.
• Databricks expertise including Delta Lake and streaming.

Responsibilities

• Design and maintain MLOps pipelines with CI/CD for ML workflows.
• Develop scalable data processing using Spark to handle large datasets.
• Manage Databricks environments and optimize costs.
• Productionize ML models via scalable APIs and batch systems.
• Implement testing, monitoring, and alerting for reliability.
• Promote engineering best practices and environment reproducibility.
• Support advertising infrastructure with data feeds for autonomous platforms.
• Drive architectural improvements for performance and scalability.

Skills

Python

Apache Spark

Scala

English

French (asset)

Tools

GitHub Actions

Jenkins

Azure DevOps

GitLab CI

ArgoCD

Databricks

Delta Lake

Kubernetes

OpenShift

Job description

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 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.