Job Description
Through its core values of Courage, Respect and Excellence, CAMH is implementing its Strategic Plan: Connected CAMH, to transform lives, ignite innovation and discovery, revolutionize education and drive social change. CAMH is more than a hospital, it is a cause. CAMH is on a mission to change the way society thinks about and responds to mental illness. They aim to eliminate prejudice and discrimination and shape a world where mental illness is central to our healthcare system – a world where Mental Health is Health.
To learn more about CAMH, please visit their website at: www.camh.ca.
To view our Land Acknowledgment, please click here.
The Krembil Centre for Neuroinformatics (KCNI) at the Centre for Addiction and Mental Health (CAMH) is leveraging Artificial Intelligence (AI) to analyze large amounts of multi-modal brain and mental health data. Our goal is to accelerate our understanding of mental illness and addictions, and to advance diagnosis, management and therapeutic interventions. Our research is conducted in an open science and collaborative environment, with a strong emphasis on patient-centered and comprehensive care.
The “Brain Health Data Challenge (BHDC) Platform” aims to transform brain research by supporting the development of AI prediction models for numerous brain diseases. This will be achieved by organizing AI challenges using Canadian datasets spanning the entire lifespan. The BHDC platform promotes reproducible, equitable and open AI model frameworks.
The BHDC team is looking for two (2) full-time, contract (1 year) Data Engineers to join our team. Reporting to the Principal Investigator, you will play a crucial role in managing the entire data lifecycle within the project for challenge organization. This includes data ingestion and deposition into a data platform, data preparation, documentation and dissemination to challenge users, while ensuring data integrity and alignment with Canadian privacy laws and healthcare data sharing practices. The Data Engineer will collaborate with various challenge teams to support the organization of diverse data types by constructing and maintaining data systems and databases.
Key Accountabilities

Data Ingestion and Deposition:

• Coordinate the ingestion of data from diverse sources and ensure proper configuration to challenge platform.
• Collaborate with knowledge users (e.g., data scientists and clinical researchers) to understand data requirements and implement technical infrastructure and database architectures that power an advanced and secure data ecosystem.
• Develop, or more importantly, integrate existing tools (e.g., ML and AI pipelines) to build scalable and rapidly deployable environments which store datasets and allow for efficient data retrieval in challenges.
• Implement and optimize data storage solutions using databases (SQL, NoSQL), data lakes, or data warehouses.
• Implement cloud-native practices and infrastructure-as-code principles to ensure highly scalable, secure, and cost-effective data solutions, tailored to support modern AI workflows.

Data Cleaning, Preparation and Documentation:

• Clean and preprocess raw data to ensure accuracy, consistency and reliability.
• Handle missing values, outliers and inconsistencies in the data.
• Optimize data pipelines, queries and algorithms for better performance and scalability.
• Develop ETL (Extract, Transform, Load) processes to transform raw data into a usable format.
• Document data engineering processes, workflows and data dictionaries to enhance data literacy among users.
• Provide documentation to help challenge users adopt to challenge platform effectively.
• Continually improve data engineering processes and workflows to enhance efficiency and effectiveness.

Technical Support for ML Use:

• Ensure infrastructure and data access needs are met to facilitate machine learning workflows, including troubleshooting and user support.
• Provide mentorship on data best practices, cultivating a culture of data ownership and stewardship across users to improve overall organizational effectiveness.

Data Security and Compliance:

• Establish secure, governed access mechanisms that meet ethical and legal standards for data sharing for secondary use (e.g., GDPR, HIPAA, and Canadian regulations).
• Implement comprehensive security measures to protect sensitive data from unauthorized access or breaches, including authentication and authorization (e.g., OAuth 2.0, Keycloak), PHI masking, audit logging and alerts.

General Responsibilities:

• Perform cross-functional or other duties as assigned and/or requested.
• Maintain a work environment that embraces diversity and is free of harassment and discrimination.

The position is based at the Krembil Centre for Neuroinformatics, located at 250 College Street.

Job Requirements

• Bachelor’s degree in Computer Science, Data Science, Mathematics, Bioinformatics or a related field.
• 3 years of experience as a Data Engineer or similar role focusing on data integration and platform management, preferably in the context of healthcare or mental health domain.
• Knowledge of data models and data structures for optimizing data processing workflows, and implementing efficient algorithms.
• Understanding of ETL processes and tools for extracting, transforming and loading data from various sources into a unified format.
• Experience with a range of data technologies is required, which may include but are not limited to several of the following:
• Big data frameworks such as Apache Airflow, Apache Spark and/or Apache Ni-Fi.
• Data APIs and formats (e.g., Apache Parquet, REST).
• Cloud platforms (e.g., Azure, AWS).
• Database management systems, and querying languages (e.g., SQL) for data integration, storage, retrieval and manipulation.
• Programming languages such as Python (e.g., Pandas, Scipy and/or Numpy).
• Data visualization libraries such as Matplotlib and/or Plotly for quality checks and creating dashboards.
• Linux environment, including bash scripting, version control systems (e.g., Git), package management, and adherence to DevOps best practices.
• Experience with AI/ML pipeline engineering and analysis is an asset.
• Knowledge of mental health data types (EEG, genomics, imaging, and electronic health records) is an asset.
• Strong analytical and problem-solving skills to address technical and conceptual data challenges.
• Meticulous attention to detail to ensure accuracy and reliability of data and database systems.
• Excellent written and oral communication skills for effectively conveying technical concepts and collaborating with interdisciplinary teams.
• Demonstrate ability to multi-task under pressure, deal with conflicting priorities and meet project timelines.

You value research and recognize the transformative impact it can make on patient’s lives. The successful candidate will support and implement anti-oppressive and anti-racist best practices in the workplace. The ability to work with knowledge users of diverse identities and ethno-racial and cultural backgrounds is required.

• Perform cross-functional or other duties as assigned and/or requested.
• Maintain a work environment that embraces diversity and is free of harassment and discrimination