About the role
Role - Data Engineer Location - Canada -Remote
Bachelor's degree in related discipline and 4+ years of experience in data engineering or architecture. The right candidate could also have a different combination, such as a master's degree and 2 years' experience; a Ph.D. and up to 1 year of experience; or 16 years' experience in a related field.
4+ years of hands-on experience in data engineering, business intelligence, and/or enterprise analytics across multiple functional areas (reporting, dashboards, data pipelines, and data modeling).
Advanced SQL proficiency and experience with data integration tools (e.g., MS SQL Developer, dbt, Informatica).
Must have strong working experience with data modeling, data access, schemas, and data storage techniques within Snowflake.
Working experience in design, development, and implementation of scalable data pipelines in cloud environments (AWS, Snowflake).
Working experience with ETL/ELT patterns, data warehousing concepts, and data orchestration tools.
Working experience working with relational databases such as SQL, MySQL, Postgres/PostgreSQL.
Working experience with business intelligence tools and platforms (Power BI, Tableau, or similar).
Working experience with data quality tools.
Working experience with application lifecycle methodologies (e.g. waterfall, agile, iterative).
Demonstrated project management experience with complex system implementations.
Experience working with Git.
Experience working with AI related tools such as GitHub Copilot, Snowflake Cortex, or M365 Copilot.
Experience implementing new tools into environments.
Excellent analytical, problem-solving, and communication skills with the ability to present technical concepts to non-technical stakeholders.
Preferred Qualifications:
Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) for data transformation and predictive modeling.
Familiarity with data orchestration tools such as Apache Airflow, dbt, or Dagster.
Hands-on experience with cloud-native data platforms (e.g., Snowflake, AWS Redshift, Azure Synapse).
Knowledge of data governance and metadata management best practices.
Experience integrating external data sources and APIs into enterprise data ecosystems.
Strong understanding of CI/CD pipelines and DevOps practices for data engineering.
Ability to work in Agile environments and contribute to sprint planning and backlog grooming.
Exposure to real-time data streaming technologies (e.g., Kafka, Kinesis) is a plus.
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About the role
Role - Data Engineer Location - Canada -Remote
Bachelor's degree in related discipline and 4+ years of experience in data engineering or architecture. The right candidate could also have a different combination, such as a master's degree and 2 years' experience; a Ph.D. and up to 1 year of experience; or 16 years' experience in a related field.
4+ years of hands-on experience in data engineering, business intelligence, and/or enterprise analytics across multiple functional areas (reporting, dashboards, data pipelines, and data modeling).
Advanced SQL proficiency and experience with data integration tools (e.g., MS SQL Developer, dbt, Informatica).
Must have strong working experience with data modeling, data access, schemas, and data storage techniques within Snowflake.
Working experience in design, development, and implementation of scalable data pipelines in cloud environments (AWS, Snowflake).
Working experience with ETL/ELT patterns, data warehousing concepts, and data orchestration tools.
Working experience working with relational databases such as SQL, MySQL, Postgres/PostgreSQL.
Working experience with business intelligence tools and platforms (Power BI, Tableau, or similar).
Working experience with data quality tools.
Working experience with application lifecycle methodologies (e.g. waterfall, agile, iterative).
Demonstrated project management experience with complex system implementations.
Experience working with Git.
Experience working with AI related tools such as GitHub Copilot, Snowflake Cortex, or M365 Copilot.
Experience implementing new tools into environments.
Excellent analytical, problem-solving, and communication skills with the ability to present technical concepts to non-technical stakeholders.
Preferred Qualifications:
Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) for data transformation and predictive modeling.
Familiarity with data orchestration tools such as Apache Airflow, dbt, or Dagster.
Hands-on experience with cloud-native data platforms (e.g., Snowflake, AWS Redshift, Azure Synapse).
Knowledge of data governance and metadata management best practices.
Experience integrating external data sources and APIs into enterprise data ecosystems.
Strong understanding of CI/CD pipelines and DevOps practices for data engineering.
Ability to work in Agile environments and contribute to sprint planning and backlog grooming.
Exposure to real-time data streaming technologies (e.g., Kafka, Kinesis) is a plus.