[All remote jobs](https://jobicy.com/jobs.md)[![Jobber logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/135a74b7-221.jpeg)](https://jobicy.com/company/jobber.md)[Jobber](https://jobicy.com/company/jobber.md) # Staff Data Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/jobber.md)ShareRemote from[Canada](https://jobicy.com/job-region/canada.md)SalaryCAD 169,200–228,900 / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished9 Oct 2026Apply before8 Nov 2026Listing views30Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Jobber is hiring a Staff Data Engineer for its Data Platform/Data Integration team to build foundational data movement, transformation, and self-service capabilities. The role combines hands-on architecture and coding with technical leadership, mentorship, and cross-functional roadmap work. Core responsibilities include scalable batch and real-time pipelines, Snowflake CDC materialization, data governance, observability, reliability standards, and disaster recovery. The engineer will participate in an on-call rotation and partner closely with engineering, analytics, data science, product, and program-management stakeholders. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a staff-level platform role requiring deep expertise across distributed data systems, cloud infrastructure, reliability engineering, governance, and software architecture. The successful candidate must independently influence technical direction while leading high-impact initiatives across multiple teams. ## Salary analysis Estimated compensation compared with the broader CA market for similar roles. Estimated job medianMarket rateC$199,100CA market rangeC$170k–C$230k0C$253k AI insightThe disclosed annual base-salary range is CAD 169,200 to CAD 228,900, with an explicit midpoint of CAD 199,100; therefore the job median is CAD 199,100. The estimated US market range for a Staff Data Engineer is USD 170,000 to USD 230,000 annually, varying by geography, company stage, cloud/data-platform scope, and equity structure. The market range is an estimate and is shown in USD, while the disclosed offer is in CAD. ## Core skills Skills and capabilities most closely associated with this opportunity. [Data engineering](https://jobicy.com/jobs?search_keywords=Data%20engineering.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[ETL/ELT pipelines](https://jobicy.com/jobs?search_keywords=ETLELT%20pipelines.md)[Change data capture (CDC)](https://jobicy.com/jobs?search_keywords=Change%20data%20capture%20CDC.md)[Real-time data processing](https://jobicy.com/jobs?search_keywords=Real-time%20data%20processing.md)[Data governance](https://jobicy.com/jobs?search_keywords=Data%20governance.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md) Sample interview questionsHow would you design a scalable CDC pipeline that materializes source-system changes into Snowflake while preserving data correctness?I would start by defining delivery, ordering, schema-evolution, and recovery guarantees with source owners and consumers. I would use durable event storage, idempotent consumers, checkpointing, deduplication keys, and replayable raw data, then materialize curated Snowflake tables through tested merge patterns. I would also implement freshness, volume, and reconciliation monitoring, along with documented backfill and incident-recovery procedures. Describe a reliability initiative you would prioritize for a critical data platform. I would first identify the highest-impact failure modes through incident history, dependency mapping, and service-level objectives. A strong initiative could include defining SLIs and SLOs for pipeline freshness and successful delivery, adding end-to-end tracing and alerting, testing failure recovery, and creating runbooks with clear ownership. Success would be measured by improved data availability, lower mean time to detect and recover, and fewer downstream data-quality incidents. How do you balance hands-on technical delivery with staff-level leadership and mentorship? I stay close to the most consequential technical decisions, prototypes, and complex implementation work while delegating ownership of well-scoped components. I make architecture and trade-offs visible through design reviews, written decision records, and coaching sessions. This approach raises engineering capability across the team rather than making delivery dependent on one person. What approach would you take to building data governance without slowing teams down? I would treat governance as an enablement product rather than a manual approval process. I would provide standardized datasets, ownership metadata, lineage, access controls, data contracts, and automated quality checks directly in developer workflows. Risk-based controls and self-service templates let teams move quickly while protecting sensitive data and maintaining trust. How would you evaluate whether a new distributed processing or ML-platform technology is worth adopting? I would begin with a clearly defined business or platform problem and compare the proposed tool against existing capabilities, operating cost, reliability, security, and team support requirements. I would run a time-boxed proof of concept with measurable success criteria such as latency, throughput, developer experience, and maintenance burden. Adoption should follow only if the evidence shows meaningful long-term value and there is a clear ownership and migration plan. Opportunity details ## About this role. Please note: Our Talent team at Jobber will be offline for Jobber Impact – our flagship event of the year, bringing Jobberinos together to connect, learn, and celebrate what we’re building – starting October 6th and extending until October 9th. We’ll be back to go through applications and reach out with an update on your candidacy once we return next week! * This will be updated automatically on all job posts above our What you can expect from Jobber headline. * The automated email to candidates will have the same message as above. ### 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[for our customers](https://www.youtube.com/watch?v=EkfZmHrRLx4&list=PLoktA_D-2UEnxMLfCe93bANKXVXd2bdN8&index=24). 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 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. Jobber is committed to maintaining a fair, transparent, and humane on-call experience. Your interview team will be happy to walk you through how we manage on-call responsibilities in practice. 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 (e.g., 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. * Knowledge of data privacy, security, and compliance considerations in a cloud-based data environment. 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 $169,200 CAD, a midpoint of $199,100 CAD and a maximum salary of $228,900 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! What you can expect from Jobber: * A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, matching in RRSP, TFSA or FHSA, and stock options. * A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential. * A unique opportunity to build, grow, and leave your impact on a $400-billion industry that has no dominant player…yet. * To work with a group of people who are humble, supportive, and give a sh*t about our customers. We believe that diverse teams perform better and that fostering an inclusive work environment is a key part of growing a successful team. We welcome people of diverse backgrounds, experiences, and perspectives. We are an equal opportunity employer, and we are committed to working with applicants requesting accommodation at any stage of the hiring process. A bit more about us: Job by job, we’re transforming the way service is delivered. Your lawn care provider, home cleaning service, plumber or painter could use Jobber to better connect with their customers, save time in the office, invoice faster, and get paid! We’re bringing tens of thousands of people together with technology to deliver billions of dollars a year in services to happy customers. Jobber exists to help make these small businesses successful, and when they’re successful we all win! 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