Sr Data Engineer (Toronto, Canada)

Job Posting

TL Development : Write new ETLs for real-time and batch data to support reporting and product features.

Requirement Gathering & Design : Collaborate with product teams to understand needs, design solutions, and document implementation plans.

System Development : Build monitoring, alerting, and deployment pipelines; ensure performance and cost-efficiency.

Best Practices : Apply standards for test coverage, scalability, fault tolerance, and system architecture.

Documentation : Create system overviews, runbooks, and improvement recommendations.

Agile Project Management : Participate in stand-ups, manage blockers, track progress, and communicate with stakeholders.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity / affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and / or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@.

Skills and Requirements

3-5+ years in software / data engineering using Agile and DevOps.

Building Data Lakes / Warehouses with big data and cloud tech.

Technical Skills :

• End to end ETL work flows
• Kafka for real time data streaming / data ingestion
• Spark for real time data for aggregation and computational data engineering
• Databricks for clustering and other
• AWS and S3 buckets (they don't use lambdas or anything rn)
• Python and Pyspark
• SQL for querying
• Experience with centralizing analytics and data for insights for other teams to create roadmaps and direction
• ML Experience deploying ML models
• CICD or Devops (they have another team to support this but nice to have)