Staff Data Engineer, Jobber

• 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

Desirable

• We’re looking for a new Staff Data Engineer to join our Data Platform Team
• The Data Platform team is responsible for managing the critical data stores and systems in which enable orchestration and transformation of data to power; analytics, machine learning and reporting at Jobber
• Data Platform empowers teams across the organization to fully leverage data, tools, and technology to achieve their goals while ensuring that we uphold high data quality standards and governance
• You’ll research, develop and maintain data systems and provide essential operational and analytical support to ensure Jobber’s internal teams are set up for success
• As a Staff Data Engineer at Jobber, you will play a critical role in shaping the future of our data platform
• 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
• 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