[Hiring] Senior Data Engineer @Babylist

Role Description

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.

Qualifications

• 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 genuinely excited about what AI can do - not just as a concept, but as something you want to get your hands on.

Requirements

• 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.
• Partner with product, analyst, and ML teams to deliver end-to-end data solutions — from ingestion to advanced analytics and AI.

Benefits

• Company-paid medical, dental, and vision insurance.
• Retirement savings plan with company matching and flexible spending accounts.
• Generous paid parental leave and PTO.
• Winter Wonder Week: the whole company takes a paid week off at the end of the year, whether or not you celebrate anything, so everyone's out at once and comes back refreshed.
• Remote work stipend to set up your office.
• Perks for physical, mental, and emotional health, parenting, childcare, and financial planning.