Senior Data Engineer, ML Platform

Are you ready to take the ML Platform to the next level and help MLEs move 10x faster?

Then Jobber might be the place for you! We’re looking for a Senior Data Engineer to be part of our ML Platform team.

Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber they can quote, schedule, invoice, and collect payments from their customers, while providing an easy and professional customer experience. Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. That’s why we put the power and flexibility in their hands to run their businesses how, where, and when they want! 

Our culture of transparency, inclusivity, collaboration, and innovation has been recognized by Great Place to Work, Canada’s Most Admired Corporate Cultures, and more. Jobber has also been named on the Globe and Mail’s Canada’s Top Growing Companies list, and Deloitte Canada’s Technology Fast 50™, Enterprise Fast 15, and Technology Fast 500™ lists. With an Executive team that has over thirty years of industry experience of leading the way, we’ve come a long way from our first customer in 2011—but we’ve just scratched the surface of what we want to accomplish for our customers.

We help employees grow professionally; we have a ton of onboarding resources, tutorials, hackathons and buddies to support learnings and provide opportunities to innovate. We have a range of experience levels on teams which allows for mentor/mentee opportunities. Leaders at Jobber work with empathy and support employees to build healthy work-life harmony. Bring your dedication and passion to this job to fulfill your goals

The team:

The ML Platform team at Jobber is at the beginning of an exciting journey—building a scalable, self-serve platform that empowers data scientists and ML engineers to deliver models faster and scale with ease. We’re laying the foundation for ML across key functions like Revenue, Marketing, Finance, Sales, Support, and Risk.

This is a rare opportunity to shape the foundations of the ML platform at Jobber.  If you’re excited to blend software engineering, data engineering,  infrastructure, and knowledge of machine learning into a high-leverage role, we’d love to hear from you.

The role:

Reporting to the Director of Data at Jobber, the Senior Data Engineer will be a part of the ML Platform team at Jobber that builds and enhances the various components of the ML Platform to improve ML model development workflows. In this role, you will enable ML Engineers and Data Scientists to easily explore data, build pipelines to land features, test models and deploy to production.

The Senior Data Engineer, ML Platform will:

• Advance Our ML Platform: Continue enhancing our Ray-based ML Platform by building capabilities that reduce friction across the entire ML lifecycle—from experimentation to deployment and monitoring.
• Streamline Model Deployment: Design and implement tools and workflows to significantly shorten the time it takes to get models into production, ensuring both scalability and reliability.
• Enable Multi-Stage Environments: Expand our platform’s environment infrastructure to support seamless development, staging, and deployment of multiple model versions with robust testing pipelines.
• Collaborate Cross-Functionally: Partner with data scientists and ML engineers to gather requirements, define success criteria, and deliver on project milestones across the model development lifecycle.
• Build & Maintain Inference Pipelines: Design, implement, and operate production-grade data pipelines that support model inference while meeting defined SLOs for performance and reliability.
• Evolve the Feature Store: Proactively develop capabilities for our feature store to support a diverse range of ML models, including those built with deep learning frameworks.
• Optimize Infrastructure: Continuously evaluate and improve the performance, reliability, and cost-efficiency of our MLOps stack.
• Establish Best Practices: Define and enforce standards for version control, testing, CI/CD, and monitoring to promote reproducibility, maintainability, and trust in ML systems.

To be successful, you should have:

• Strong programming skills in Python, with a background in software or data engineering.
• Practical experience with data transformation, modeling, and workflow orchestration, using tools such as dbt, Airflow, or similar technologies to build modular, testable data pipelines.
• Expertise in containerization and CI/CD practices, particularly with Docker and modern deployment pipelines.
• Experience designing and implementing RESTful APIs that enable scalable, maintainable, and well-documented ML services.
• A proven track record of platform-building, especially systems that empower stakeholders through self-serve capabilities and streamlined user experiences.
• Strong grasp of DevOps principles, including infrastructure-as-code, version control, testing, and automated deployment workflows.
• Excellent problem-solving abilities and attention to detail, particularly when working with complex, data-intensive systems.
  
  

It would be really great (but not a deal-breaker) if you had:

While not required, experience in the following areas would be a strong asset and help you ramp up quickly in this role.

• Hands-on experience building ML platforms or MLOps infrastructure, especially in early-stage environments.
• Familiarity with Ray or other distributed computing frameworks.
• Experience with caching strategies and tools to optimize data access, reduce latency, and improve performance of ML workloads.
• Working knowledge of vector databases and their role in powering ML use cases such as semantic search and recommendation systems.
• Experience with search technologies like Elasticsearch for efficient indexing, retrieval, and analytics.
• Understanding of ML model serving frameworks (e.g., TorchServe, TensorFlow Serving) and A/B testing methodologies for evaluating model performance in production.
• Contributions to open-source MLOps or ML tooling communities.
  Familiarity with ML model versioning tools such as MLflow, DVC, or similar systems for tracking experiments and deployments.
  
  

Compensation:

At Jobber, we also believe that compensation should be transparent, fair, and supportive of your experience and growth. This role has a minimum annual salary of $125,800 CAD, a midpoint of $147,900 CAD, and a maximum salary of $170,100 CAD, designed to reflect the progression from learning the ropes to truly excelling. 

We design our compensation to reflect each new hire's skills, experience, and the complexity of the role, ensuring a fair and competitive salary. Our range is intentionally broad to support growth and long-term impact, with fully established hires typically starting around the midpoint. The higher end of the range is reserved for those who have demonstrated deep expertise and lasting contributions, while offers below the midpoint reflect strong potential with room to develop. This approach ensures that compensation aligns with both an individual's current capabilities and their opportunity for future growth.

Base salary is just one part of a total compensation package that will include equity rewards, annual stipends for health and wellness, retirement savings matching, and an extended health package with fully paid premiums for body and mind. Your professional growth matters to us too! You'll have access to a dedicated talent development program that includes career coaching and opportunities for career development.

We believe in transparency and open conversations about compensation. If you have any questions about our approach, we're happy to discuss them throughout the hiring process!