Data Engineer

What the Role Is

The Data team at Babylist powers decision-making across the entire business — from product growth to operations to AI. This is a senior individual contributor role on the Data Engineering team, sitting at the intersection of platform thinking and AI-native tooling. You'll own the architecture and systems that make data engineering itself more scalable — not just the pipelines, but the harnesses and agentic scaffolding that generate, test, and maintain them. You'll work cross-functionally with analysts, data scientists, and product teams to keep Babylist's data infrastructure reliable and ahead of where the business is going.

Who You Are

• Experienced building production AI/LLM systems — RAG pipelines, agentic workflows, tool integrations (e.g., MCP servers) that real users depend on, not just prototypes
• Platform-minded — you see repetitive data engineering work as a system design problem and build the scaffolding to automate it
• 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
• Comfortable in AWS — you've provisioned and managed cloud data resources across EC2, S3, Lambda, and EKS
• Familiar with Snowflake or comparable modern cloud data warehouses
• Able to work cross-functionally with analysts and data scientists — translating their needs into infrastructure that actually serves them
• 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.

How You Will Make An Impact

• Build and scale data pipelines for ingestion into Snowflake — with a focus on reliability and performance across a $750M+ e-commerce business
• Design and ship the agentic systems that perform data engineering work — pipeline generation, testing, and maintenance as automated systems, not one-off builds
• Develop and maintain ML pipelines that help data scientists operationalize models and integrate them with our data infrastructure
• Implement and improve data monitoring across complex, multi-system user journeys
• Collaborate with Analytics Engineers on data modeling and the reliability of shared data assets