Staff Data Engineer

This role is open to being 100% remote within Canada while our home office is located in Toronto, Ontario. You may be required to travel to Toronto once per quarter for team and/or company events.

Who You’ll Be Joining:

You will be part of the dynamic data engineering and machine learning services group at ecobee focused on leveraging data to enhance the smart home experience for customers. This team is responsible for building and maintaining the data infrastructure and machine learning capabilities that power intelligent features across ecobee’s product ecosystem, such as integrated AI services, energy optimization, home automation, personalized climate control, predictive maintenance.

How You’ll Make an Impact:   

• Cross-Domain Problem Solving: Lead the design and implementation of scalable data pipelines and systems for complex problems that require detailed understanding across multiple domains (e.g., data, machine learning, IoT, cloud infrastructure). These problems will often come with high levels of ambiguity, incomplete data, and evolving requirements.
• Architectural Impact: Contribute to ecobee’s system architecture with designs that have been battle-tested, resulting in significant, long-lasting impact within a specific domain. Solutions are expected to integrate elegantly with ecobee’s broader enterprise architecture and align with company-wide standards.
• Enterprise-Wide Architecture: Start to think beyond individual components or domains, considering ecobee’s broader architectural strategy. Collaborate with principal engineers and directors to ensure designs complement the company’s vision.
• Technical Proposals: Propose technical solutions and strategies that have a significant impact on ecobee’s data ecosystem. These solutions should drive improvement in the scalability, performance, and resilience of the company’s products and services.
• Component Ownership: Take end-to-end ownership of full components within your domain of expertise, ensuring that their design, implementation, testing, deployment, and operations meet high standards. These components will likely interact with systems in other domains, requiring careful consideration of cross-team dependencies.
• System Operations & SLAs: Define and track SLAs for the components you own, ensuring they meet operational excellence standards and contribute to the system’s overall reliability.
• Maintainability & Scalability: Systematically consider maintainability in designs and implementations, with a focus on ensuring systems can scale to support ecobee’s growing data needs.
• Mentor & Lead: Actively mentor engineers across the organization, helping them achieve concrete technical and professional goals. Drive knowledge-sharing initiatives through code reviews, technical talks, and training sessions.
• Cross-Team Collaboration: Facilitate and guide technical discussions across squads, ensuring decisions are aligned with ecobee’s strategic goals. You’ll help foster an inclusive environment where all team members feel heard and respected.
• Technical Expertise Development: Participate in “bar-raiser” groups that focus on elevating engineering standards across ecobee, including leading post-mortem reviews, design sessions, and code reviews.
• Challenging Best Practices: Continuously review existing processes, best practices, and rituals across ecobee’s engineering organization. Propose and implement improvements that enhance efficiency, collaboration, and quality.
• Delivery Metrics & Quality: Educate teams on key software delivery metrics and help track progress. Ensure that the team’s testing approaches align with accepted frameworks, and work to close gaps in quality metrics.
• Documentation & Knowledge Sharing: Foster a culture of documentation and transparency within the team and across stakeholders, ensuring that key processes and decisions are well-documented and accessible.
• Forward-Thinking Design: Anticipate future data challenges, such as scalability and security concerns, and propose strategies to avoid roadblocks. You’ll look for opportunities to improve existing solutions and identify novel approaches that haven’t been tried before.
• Technology Evaluation: Stay ahead of industry trends by evaluating and recommending new technologies that align with ecobee’s goals in data engineering, machine learning, and IoT.
• Domain-Wide Impact: Your work will have a measurable impact across multiple teams within the Data Engineering & Machine Learning Services group. This impact will often have significant customer implications, driving improvements in performance, scalability, and product capabilities.
• Economic Thinking & Risk Management: Drive a culture of thoughtful decision-making, balancing technical innovation with practical constraints like time, cost, and risk. Work closely with partner teams to prioritize capabilities that will deliver the highest business impact.
• Proactive Issue Resolution: Anticipate blockers and delays in projects before they require escalation. Proactively work to resolve these challenges by engaging with stakeholders and partner teams.

What You’ll Bring to the Table:  

• 10+ years of experience in data/software engineering, with a proven track record of owning and delivering complex, cross-domain projects at scale.
• Extensive experience in building and maintaining scalable data pipelines and architecture using tools like Apache Spark, Kafka, and Airflow.
• Expertise in cloud data platforms (AWS, GCP, or Azure), with a strong focus on distributed systems, cloud managed open source frameworks and services, and IoT data integration.
• Solid understanding of end-to-end data systems, from ingestion to machine learning model deployment and inference.
• Expertise in data security, data governance, and compliance regulations relevant to the industry.
• Extensive experience in data architecture, database design and data engineering methodologies across multiple industries, with at least 5 years in a technical leadership role.
• Ability to solve problems that span multiple domains, including data engineering, machine learning, IoT, and cloud infrastructure. A deep understanding of how these domains interact is essential.
• Experience with real-time data processing, analytics platforms, and machine learning integration is highly valued.
• Proven ability to mentor and guide engineers, from juniors to senior engineers, across multiple teams. Experience facilitating technical discussions and driving consensus.
• Demonstrated ability to lead cross-functional initiatives and work effectively across squads.
• A strategic mindset, with the ability to think ahead about potential roadblocks and design systems that can scale and evolve with ecobee’s needs.
• Experience driving large technical initiatives from ideation through implementation, with a focus on creating systems that deliver high business impact.
• Demonstrated track record of contributing to new processes and practices within engineering teams. You’re comfortable challenging the status quo and driving improvements.
• Experience with software delivery metrics and ensuring that teams follow best practices in testing, code quality, and maintainability.

Just so you know: The hired candidate will be required to complete a background check. 

What happens after you apply:   

Application review. It will happen. By an actual person in Talent Acquisition. We get upwards of 100+ applications for some roles, it can take a few days, but every applicant can expect a note regarding their application status.  

Interview Process:

• A 30-minute phone call with a member of Talent Acquisition  
• A 45 minutes interview with the candidate with Director of Engineering. This is to discuss experience designing and building scalable data architectures, pipelines, and processing systems.
• 90 minutes with staff and senior engineers for technical interview on System Design & Architecture and coding challenge
• 1-hour with engineering leaders for a technical deep dive discussion
• The final interview itself will be a 90 minutes interview divided into two parts where you will meet with the Director of Engineering for 45 minutes followed by a 45 minute interview with the VP