Posting Reason: New Position Location: Main Campus Academic Period: 2026 Spring-Summer Semester Faculty: Faculté des sciences sociales / Faculty of Social Sciences Academic Unit: École de psychologie \\ School of Psychology Course Title: The Psychology of Adolescence Course Code: PSY3105 Section: E00 Course Description: Physical, emotional, social, moral and intellectual development of the adolescent. Problems in the development of personal, social and sexual maturity. Current research in the psychology of adolescence. Course Component: Lecture Posting limited to: Professeur à temps-partiel régulier / Regular Part-Time Professor Date Posted (YYYY/MM/DD): 2026/04/16 Applications must be received BEFORE (YYYY/MM/DD): 2026/04/23 Expected Enrolment: 147 Approval date: 2026/04/16 Number of credits: 3 Work Hours: 39 Hourly Rate: Enseignement / Teaching: $239.47 (2024-2025)The academic year starts on September 1 and ends on August 31. These rates do not included vacation pay nor statutory pay. These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification. Course type: B Posting type: Irrégulier / Irregular Language of instruction: Anglais | English Competence in second language: Passive Course Schedule: Mardi | Tuesday 13:00-16:00 Jeudi | Thursday 13:00-16:00 - This course is offered online. Requirements: Ph.D. in Psychology / Advanced knowledge of the course content area Additional Information and/or Comments: An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience. If you are invited to continue the selection process, please notify us of any adaptive measures you might require. Information you send us will be handled respectfully and in complete confidence. Employees are required under provincial law to successfully complete all mandatory legislated training. The list of training may be modified by provincial law. The hiring process will be governed by the current APTPUO collective agreements; you can click here for the main unit, here for the OLBI unit, or here for the Toronto/Windsor unit to find out more. The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect, teamwork and inclusion, where collaboration, innovation, and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply, we welcome applications from qualified Indigenous persons, racialized persons, persons with disabilities, women and LGBTQIA2S+ persons. The University is committed to creating and maintaining an accessible, barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment, assessment and selection processes. Applicants with disabilities may contact vra.affairesprofessorales@uottawa.ca to communicate the accommodation need. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority. Prior to May 1, 2022, the University required all students, faculty, staff, and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 – Covid-19 Vaccination. This policy was suspended effective May 1, 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.

• Imagine shaping the future of how data drives business decisions and AI innovation. • Build, maintain, and optimize end-to-end data pipelines using dbt, Python, PySpark, Polars, and SQL. • Operate and enhance our AWS data infrastructure, including Glue, Athena, S3, and supporting tools. • Design and evolve Apache Iceberg tables, managing schema evolution, partitioning, compaction, and efficient storage. • Drive data quality, observability, lineage, and monitoring across the entire data platform. • Translate business and analytical needs into scalable, maintainable, and high-performing data solutions. • Collaborate with data scientists, analysts, engineers, and product teams to deliver clean, trusted datasets and APIs. • Contribute to the ongoing evolution of our data mesh, developing new patterns, enhancements, and architecture improvements. • Build AI/ML-ready data environments and datasets that enable intelligent products and predictive insights. • Document systems, design decisions, and processes to ensure clarity, consistency, and smooth onboarding.

The Data Engineer is responsible for developing and maintaining data pipelines to support data ingestion, transformation, and integration within a scalable data platform. This role ensures that data from multiple sources is processed efficiently, securely, and with high quality standards. Responsibilities - Develop and maintain data ingestion pipelines from multiple sources - Build ETL/ELT processes for data transformation - Integrate structured and unstructured data into data platforms - Implement incremental and automated data ingestion processes - Ensure data quality through validations and monitoring - Perform data cleaning and normalization tasks - Optimize pipelines for performance and scalability - Document data pipelines and data structures - Collaborate with cross-functional teams to support data initiatives Requirements - Bachelor’s degree in Computer Science, Engineering, or related field - Experience in data engineering or data pipeline development - Knowledge of ETL/ELT processes and data integration concepts - Experience with cloud-based data platforms (AWS or similar) - Experience working with APIs and database integrations - Proficiency in SQL and scripting languages (Python or similar) - Familiarity with distributed data processing frameworks Nice to Have - Experience with AWS data services - Knowledge of infrastructure as code tools - Familiarity with data lake architectures - Experience with Spark or similar technologies - Experience with data pipeline orchestration tools Soft Skills - Analytical thinking - Problem-solving skills - Collaborative mindset - Attention to detail

Lead Data Architect Location: Remote, US based Position type: Full Time Employee Compensation: $190.000 (base + bonus) Job Summary Data Meaning is a front-runner in Business Intelligence and Data Analytics consulting, renowned for our high-quality consulting services throughout the US and beyond. Our expertise lies in delivering tailored solutions in Business Intelligence, Data Warehousing, and Project Management. We have a diverse, global team of consultants, all working remotely, embodying a collaborative, inclusive, and innovation-driven work culture. As part of our growth and upcoming initiatives, we are proactively searching for Lead Data Architects to support future data platform modernization initiatives across our client portfolio. Position Summary The Lead Data Architect will lead the architecture assessment, transition planning, and modernization of the client’s data platform. This role will be responsible for designing the future-state architecture, defining best practices for ELT pipelines, and guiding the migration of existing SQL-based transformations into a scalable dbt framework within a Snowflake environment. The position will work closely with the client’s engineering teams and internal Data Meaning consultants, leading knowledge transfer sessions, architecture walkthroughs, and platform transition initiatives to ensure long-term scalability, reliability, and governance of the data platform. Key Responsibilities: - Lead architecture assessment and documentation of the current data platform (Snowflake, Airflow, AWS) - Design the future-state data platform architecture following modern ELT and dbt best practices - Define standards for: data modeling, pipeline architecture, transformation frameworks, governance and data quality - Lead the migration of transformation logic into dbt models - Define architectural patterns for: staging layers, intermediate layers, data marts - Implement automated testing frameworks within dbt - Lead discovery sessions and technical workshops with the client’s data engineering teams - Document architecture, pipelines, and operational processes - Conduct architecture walkthrough sessions during knowledge transfer phases - Define standards for CI/CD, monitoring frameworks, and data quality validation - Establish schema management and governance best practices - Optimize Snowflake compute usage and platform performance - Design scalable orchestration strategies for Airflow pipelines - Improve platform reliability, monitoring, and operational observability Required Skills & Qualifications: - Advanced / fluent English - 10+ years of experience in Data Engineering or Data Architecture - Strong experience designing and implementing modern data platforms - Expert-level experience with Snowflake - Strong experience implementing dbt transformation frameworks - Advanced SQL modeling and performance optimization - Strong experience with Apache Airflow orchestration - Strong Python programming skills - Experience designing: ELT pipelines, Data warehouse architectures, Transformation frameworks, Large-scale data pipelines - Experience working in cloud environments (AWS preferred) Nice to Have - Snowflake certification - Experience leading data platform migrations or modernization initiatives - Experience implementing Medallion architecture patterns - Experience defining data governance frameworks - Experience working with distributed or global engineering teams