Senior Data Engineer, Jobber

• 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
• 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

Desirable

• We’re looking for a Senior Data Engineer to be part of our ML Platform 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
• 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
• 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