Staff Data Engineer

Does working with Data motivate and excite you? Do you want to make a difference cross-functionally?

Then Jobber might be the place for you! We’re looking for a new Staff Data Engineer to join our Data 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.

We help employees grow professionally; we have a ton of onboarding resources, tutorials, hackathons, and buddies to support learning 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 Data Integration team's mission is to empower teams across Jobber with the right data, at the right time, in the right place, so they can deliver business value better and faster. Key responsibilities include: data ingestion and Change Data Capture (CDC), including materializing CDC streams into tables in Snowflake; data activation (egress); managing the systems involved in the movement and transformation of data; self-serve tooling; and data integrity and governance.

The role:

As a Staff Data Engineer, you will play a critical role in shaping the future of data integration at Jobber. As a technical champion and force multiplier, you’ll lead and mentor a team of exceptional data engineers while solving complex technical challenges. Your expertise will span architecture, technical leadership, design, and hands-on coding, enabling you to significantly influence the direction of data at Jobber.

Beyond day-to-day delivery, you will dedicate time to work acceleration, cross-team initiatives, exploration of emerging technologies, addressing technical debt, and investing in the future of engineering at Jobber. This is a highly strategic and hands-on position where you’ll combine deep technical expertise with leadership to design resilient systems, empower teams across the organization to self-serve with confidence, and ensure our data remains a trusted asset that accelerates business growth.

As a Staff Data Engineer, you will:

• Shape Foundational Data Components: Design, build, and maintain scalable batch and real-time data pipelines, while also looking across systems and ahead to anticipate future needs.

• Demonstrate Technical Mastery: Deliver high-quality solutions through deep expertise in modern data tools and technologies. Champion technical excellence within the team by setting best practices, raising the bar for engineering quality, and mentoring team members at all levels to support their growth and career development.

• Drive Reliability & Resilience: Establish testing and reliability standards, SLAs (uptime, RTO, RPO), and disaster recovery playbooks. Lead major reliability initiatives to minimize downtime and protect critical business data.

• Advance Observability & Governance: Build frameworks for monitoring, logging, lineage, and auditing to ensure visibility, compliance, and trust in data. Define governance policies that enforce data integrity, availability, and reliability across the platform.

• Accelerate and Empower Data Access: Develop self-service tools, frameworks, and automation that reduce manual effort, improve efficiency, and enable teams across engineering, analytics, and data science to work effectively with data while minimizing dependency on the Data Platform team.

• Contribute to Strategic Planning: Partner with Technical Program Managers to define and refine strategic roadmaps, ensuring that data engineering priorities align with business objectives.

• Drive Cross-Team Collaboration: Collaborate with Staff Engineers and technical leaders across domains to identify friction points, and work collectively to design solutions that improve system reliability, consistency, and scalability.

• Accelerate Business Growth: Work closely with data analysts, scientists, and product teams to enable fast, seamless exploration, analysis, modeling, and reporting. Build automation and infrastructure that reduce friction and accelerate decision-making.

• Safeguard Data Integrity: Own the integrity and reliability of data, ensuring stakeholders across the organization maintain trust in the insights and decisions driven by it.

• On-Call Rotation: Members of the Data Platform team participate in an on-call rotation, covering one week at a time. When an incident occurs outside of regular working hours, we provide time off in lieu to support healthy balance and recovery. From time to time, major maintenance work may require team members to serve as primary or secondary on-call support over a weekend.

To be successful, you should have:

• Core Data Engineering Expertise: Hands-on experience with batch and real-time data processing frameworks, lakehouse/warehouse management, large-scale data transformation, data serialization, workflow orchestration, and dimensional modeling (star/snow-flake schemas).

• Scalable Systems Development: Proven ability to design and deliver highly scalable, maintainable, and high-performance solutions across multiple layers of the technology stack, leveraging containerization, CI/CD, and API development.

• AWS Cloud Proficiency: Strong understanding of AWS services relevant to the data domain, with hands-on experience leveraging them to design and implement data solutions.

• Technical Leadership & Engineering Excellence: Demonstrated success guiding teams through complex, high-impact projects while providing architectural direction and serving as a trusted technical lead. Exceptional proficiency in software design, system architecture, and coding, with a focus on long-term maintainability, performance, and resilience.

• Reliability & DevOps Practices: Strong background in infrastructure-as-code (Terraform, CloudFormation), observability (logging, monitoring, tracing), and system reliability.

• Collaboration & Adaptability: Exceptional communication skills, self-motivation, and resourcefulness, with the ability to navigate ambiguity, prioritize effectively, and deliver results in fast-paced environments.

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

• Machine Learning Platform Experience: Exposure to ML platforms and distributed compute frameworks (Ray, TensorFlow, PyTorch). Experience collaborating with Data Scientists to operationalize models, implement drift detection, or scale ML workloads.

• Cross-Domain Engineering Experience: Hands-on exposure to non-data engineering codebases, such as web application frameworks (Ruby on Rails) and modern front-end stacks (TypeScript/React).

• API & Integration Knowledge: Familiarity with GraphQL, API layer design, and performance optimization.

• Platform Building Experience: Prior work on developer tooling or shared platforms that supported multiple engineering domains.

• Governance & Compliance Awareness: Knowledge of data privacy, security, and compliance in cloud-based data environments.