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Senior Data Engineer

Babylist · Ottawa, Canada

RemoteImported listingfull-time21 days ago

About The Role

Join Babylist, a rapidly growing e-commerce platform, as a Senior Data Engineer. In this role, you will be responsible for building and scaling data pipelines, designing automated systems for data engineering, and collaborating with cross-functional teams to deliver end-to-end data solutions. You will have a direct influence on the scalability of our data systems during a pre-IPO growth moment. Enjoy a range of benefits, including flexible paid time off, parental leave, and wellness benefits.

  • Architect and scale data engineering systems, focusing on pipeline generation, testing, and maintenance.
  • Collaborate cross-functionally with analysts, data scientists, and product teams to enhance data infrastructure.
  • Develop and maintain machine learning pipelines, ensuring integration with data infrastructure.
  • Comfortable in AWS — you've provisioned and managed cloud data resources across EC2, S3, Lambda, and EKS
  • Deeply fluent in Python and production-grade data engineering, with 7+ years building systems that hold up at scale
  • Proficient with Airflow and dbt — you understand data modeling and ETL principles, not just the tooling
  • Able to work cross-functionally with analysts and data scientists — translating their needs into infrastructure that actually serves them
  • Platform-minded — you see repetitive data engineering work as a system design problem and build the scaffolding to automate it
  • Familiar with Snowflake or comparable modern cloud data warehouses
  • You naturally reach for AI in your work — at Babylist, every team uses AI daily. You're already using it to move faster and improve your output, and you stay curious about what's coming next
  • You're genuinely excited about what AI can do - not just as a concept, but as something you want to get your hands on. At Babylist, every team uses AI daily, and we're looking for people who lean in
  • Experienced building production AI/LLM systems — RAG pipelines, agentic workflows, tool integrations (e.g., MCP servers) that real users depend on, not just prototypes

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