Senior 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 focused 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:   

• Design, build, and maintain scalable and efficient ETL/ELT pipelines for both batch and real-time data ingestion and transformation.
• Implement data extraction and processing solutions to support analytics, machine learning, and operational use cases.
• Integrate diverse data sources, including IoT device data, third-party APIs, and internal systems, into centralized data repositories.
• Develop and maintain data warehousing solutions and ensure data is structured and available for downstream analytics.
• Monitor and optimize data workflows and infrastructure to ensure high performance and reliability.
• Implement monitoring, alerting, and logging for data pipelines to proactively identify and resolve issues.
• Collaborate with data scientists, analysts, product managers, and other engineering teams to understand data requirements and deliver high-quality data solutions.
• Translate business requirements into technical specifications and provide guidance on data engineering best practices.
• Implement data quality checks, validation, and cleansing procedures to ensure data integrity and accuracy.
• Create and maintain comprehensive documentation for data pipelines, architectures, and processes.
• Share knowledge and best practices with the team, and contribute to the growth and development of the data engineering community within the organization.
• Architect and implement sophisticated data pipelines that handle massive IoT data streams, ensuring data quality, consistency, and low-latency processing.
• Introduce frameworks and best practices for feature engineering, data versioning, and experimentation in collaboration with machine learning teams.

What You’ll Bring to the Table:    

• Proficiency in building data pipelines using Python, SQL, and tools like Apache Spark, Apache Kafka, and Apache Airflow.
• Experience with cloud-based data platforms (GCP preferred), including services like Big Query, Big Table, and Dataflow
• Familiarity working with SQL based operational databases like
• Familiarity with data processing and storage solutions tailored for machine learning workflows.
• Good understanding of the machine learning lifecycle and experience in supporting data preparation, feature engineering, and model deployment processes.
• Experience working with machine learning frameworks and libraries is a plus.
• Strong experience in data modeling, schema design, and optimization for data warehousing and data lake solutions.
• Experience with designing data solutions that support both batch and real-time processing requirements.
• Excellent communication skills, with the ability to work effectively in a collaborative environment and convey technical concepts to non-technical stakeholders.
• Proven track record of working in cross-functional teams and driving alignment between technical and business goals.

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 (4 stages):  

• A 30-minute phone call with a member of Talent Acquisition  
• A 45 minute call with the Director of Data Engineering and Machine Learning services focused on behavioural, situational and culture fit questions
• A 90 minute virtual interview with a cross-functional group of engineers - this will be technical interview where you will be presented with a case study to solve a real life problem. This interview will test your design and coding skills necessary to succeed in this position.
• The final interview will be a 45 minute interview with leadership.