DevOps Data Engineer
On-site · Toronto, Ontario, Canada
Job Summary
Designs, develops, and optimizes data solutions to deliver high-quality data assets, reporting, and BI. Manages end-to-end data pipelines—from sourcing, lineage, quality, transformation, to storage—building container-based solutions, pipelines, APIs/microservices, and analytics portals for heterogeneous data sources on internal data lakes or cloud platforms. Collaborates with data scientists, process engineers, and business stakeholders to enable prescriptive and predictive analytics through consolidated data. Requires leadership in identifying issues and building resilient data engineering solutions; advanced SQL; Python with relevant frameworks; and experience with cloud, MLOps tools, streaming/batch processing, and visualization technologies.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering or equivalent
- 5+ years hands-on experience in data engineering and related technologies
- Experience in building end-to-end data pipelines
- Advanced SQL and relational databases
- Python experience and framework knowledge (Flask, Django, etc.)
- Knowledge of AWS or cloud platforms
- Experience with containers and microservices (Docker, Kubernetes)
- Experience with data visualization tools and BI (Dash, Plotly, Tableau)
- Experience with ML/AI workflows (MLOps, SageMaker)
- Experience with GitHub Actions, Airflow or similar automation
- Familiarity with data technologies: Kafka, Hadoop, Spark, Trino
- Security considerations for cloud applications
- Strong collaboration with data scientists and stakeholders
- Knowledge of API development (Streamlit, Flask, REST)
- Ability to design scalable data solutions
- Experience in Agile environments
- Master’s degree in relevant fields is a nice-to-have
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