Lead Data Engineer at Toronto Hybrid
Posted Updated 5
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Summary
A senior/staff Lead Data Engineer in Toronto (hybrid) who designs and ships scalable data solutions end to end: building and maintaining data pipelines, ensuring data quality, and establishing Azure self-service analytics infrastructure. Core stack is Azure (Data Factory, Databricks, Azure DevOps) with Python, SQL, and PySpark.
Elevate your career as a Lead Data Engineer with our team in Toronto, offering a hybrid work model. Drive innovative data solutions and architecture in an agile environment.
This Senior/Staff Data Engineer role entails developing robust data solutions while collaborating with business partners and stakeholders. You'll have a minimum of 8 years of experience in software and data engineering, complemented by expertise in Azure technologies including Data Factory, Databricks, and Azure DevOps. Your impact will facilitate fast, data-driven decision-making across the organization.
Key Responsibilities: • Design and implement scalable data solutions and analytics • Independently develop and ship complex features from start to finish • Build and maintain efficient data pipelines for various needs • Ensure data quality and integrity within data pipelines • Establish Azure infrastructure for self-service analytics
Requirements: • Minimum 8 years in software/data engineering • At least 2 years in a technical lead capacity • Strong understanding of data architecture patterns • Proficiency in Python and SQL, experience with PySpark • Knowledge of CI/CD practices using tools like Git
Drive innovation and excellence in data engineering as part of our Toronto-based team.
This Senior/Staff Data Engineer role entails developing robust data solutions while collaborating with business partners and stakeholders. You'll have a minimum of 8 years of experience in software and data engineering, complemented by expertise in Azure technologies including Data Factory, Databricks, and Azure DevOps. Your impact will facilitate fast, data-driven decision-making across the organization.
Key Responsibilities: • Design and implement scalable data solutions and analytics • Independently develop and ship complex features from start to finish • Build and maintain efficient data pipelines for various needs • Ensure data quality and integrity within data pipelines • Establish Azure infrastructure for self-service analytics
Requirements: • Minimum 8 years in software/data engineering • At least 2 years in a technical lead capacity • Strong understanding of data architecture patterns • Proficiency in Python and SQL, experience with PySpark • Knowledge of CI/CD practices using tools like Git
Drive innovation and excellence in data engineering as part of our Toronto-based team.