Data Engineer (2 years term) at University of Toronto | CA | Rezi

Data Engineer (2 years term) at University of Toronto

Data Engineer (2 years term)

University of Toronto · CA

Yesterday

Data Engineer (2 years term)

University of Toronto · CA

2 days ago
Resume preview

Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

The Data Engineer plays a pivotal role in managing and optimizing our data infrastructure to support high-impact research projects. The Data Engineer will be responsible for designing, implementing, and maintaining robust ETL/ELT pipelines that ensure the efficient flow of data from various sources to data warehouses and research databases. This is a grant-based 2-year term position that is ending approximately in May 2028, with the possibility of renewal.

Responsibilities

  • Reconciling business requirements with information architecture needs for highly complex system integration
  • Analyzing and optimizing database software
  • Developing and maintaining quality control procedures
  • Analyzing, recommending, and designing highly complex software architecture
  • Designing, testing, and modifying programming code
  • Leading and planning IT projects
  • Analyzing, recommending and designing technical solutions for highly complex IT problems
  • Serving as a resource to others by providing (non-supervisory) job-related guidance

Requirements

  • Bachelor's Degree (Master's Degree preferred) in Computer Science, Information Technology, Data Engineering, or a related field or acceptable combination of equivalent experience.
  • Minimum five years recent and relevant Data Engineer experience with a strong background in ETL/ELT processes in materials, chemicals, research, and technology industry or related industries with significant research and development.
  • Experience with data pipeline tools and platforms (e.g., Apache Airflow, AWS Glue, Talend, etc.).
  • Proficiency in SQL, Python, and/or other programming languages commonly used in data engineering as well as data transformation tools (e.g. DBT).
  • Solid understanding of database management systems (RDBMS, NoSQL, etc.) and data warehousing solutions (e.g., AWS Redshift, Google BigQuery, Snowflake, Databricks).
  • Familiarity with cloud computing platforms (e.g. AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
  • Strong problem-solving skills and the ability to work in a fast-paced, research-driven environment.
  • Excellent communication skills, with the ability to collaborate effectively with cross-functional teams.

Skills

  • ETL/ELT
  • Apache Airflow
  • AWS Glue
  • Talend
  • SQL
  • Python
  • DBT
  • RDBMS
  • NoSQL
  • AWS Redshift
  • Google BigQuery
  • Snowflake
  • Databricks
  • AWS
  • Azure
  • GCP
  • Docker
  • Kubernetes
  • Data modeling
  • Schema design
  • Data architecture
  • Tableau
  • Power BI

Location

  • St. George (Downtown Toronto)

Work Type

  • Full-Time
  • Grant - Term

Experience Level

  • Minimum five years recent and relevant Data Engineer experience

Education Level

  • Bachelor's Degree (Master's Degree preferred)

Salary/Compensations

  • $103,367. with an annual step progression to a maximum of $132,188.

About the Company

  • The Faculty of Arts & Science is the heart of Canada’s leading university and one of the most comprehensive and diverse academic divisions in the world. The strength of Arts & Science derives from our combined teaching and research excellence in the humanities, sciences and social sciences across 29 departments, seven colleges and 46 interdisciplinary centres, institutes and programs.
  • The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs), also called materials acceleration platforms (MAPs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. AC Staff Scientists will advance the infield of AI-driven autonomous discovery and develop the materials and molecules required to address society’s largest challenges, such as climate change, water pollution, and future pandemics.
  • The Acceleration Consortium received a $200M Canadian First Research Excellence Grant for seven years to develop self-driving labs for chemistry and materials, the largest ever grant to a Canadian University.

Equal Opportunity

  • Candidates who are members of Indigenous, Black, racialized and 2SLGBTQ+ communities, persons with disabilities, and other equity deserving groups are encouraged to apply, and their lived experience shall be taken into consideration as applicable to the posted position.