Staff Data Engineer

Join Jobber as a Staff Data Engineer, where you will lead and mentor a team of exceptional data engineers, solve complex technical challenges, and significantly influence the direction of data at Jobber. You will design and maintain scalable data pipelines, establish testing and reliability standards, build frameworks for monitoring and governance, and collaborate with cross-functional teams to accelerate business growth. This is a highly strategic and hands-on position that combines deep technical expertise with leadership.

- Concevoir, construire et maintenir des pipelines de données évolutifs, tant en mode batch qu'en temps réel, tout en anticipant les besoins futurs. - Établir des normes de test et de fiabilité, des SLA (temps de disponibilité, RTO, RPO) et des plans de reprise après sinistre, et diriger les initiatives majeures en matière de fiabilité. - Développer des outils, des cadres et des automatisations en libre-service qui réduisent l'effort manuel, améliorent l'efficacité et permettent aux équipes de travailler efficacement avec les données.

- 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) - 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 - 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 - 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 - 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 - 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