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Babylist is a leading platform for expecting and new families, serving 10M+ users annually with a $750M revenue business spanning registry, e-commerce, health, and financial services. The Data team powers decision-making across product, operations, and AI initiatives.
In this Senior Data Engineer role, you'll be a senior individual contributor 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.
Key responsibilities include:
- Build and scale data pipelines for ingestion into Snowflake with a focus on reliability and performance across the $750M+ e-commerce business
- Design and ship 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 data infrastructure
- Implement and improve data monitoring across complex, multi-system user journeys
- Collaborate with Analytics Engineers on data modeling and 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
You'll work cross-functionally with analysts, data scientists, and product teams to keep Babylist's data infrastructure reliable and ahead of business needs. The role offers rare scope—building the meta-layer of systems that build pipelines, not just pipelines themselves. The data infrastructure is solid; you're extending and automating something that works. AI is core to the mandate, not a future aspiration. You'll have direct influence on how a 9M+ user platform scales its data systems through a pre-IPO growth moment.
Babyllist is remote-first with team members across the U.S. and Canada. The company moves fast, thinks smart, and uses AI as part of how they work every day—not as an experiment, as an expectation. The team comes together twice a year to build relationships. Culture emphasizes focused work with intentional recharge, exceptional management, and products that positively impact millions of lives.
REQUIREMENTS:
- 7+ years building production systems at scale
- 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: see repetitive data engineering work as a system design problem and build scaffolding to automate it
- Deeply fluent in Python and production-grade data engineering
- Proficient with Airflow and dbt—understand data modeling and ETL principles, not just the tooling
- Comfortable in AWS—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—translate their needs into infrastructure that serves them
- Naturally reach for AI in your work; already using it to move faster and improve output; stay curious about what's coming next
- Genuinely excited about what AI can do—not just as a concept, but as something you want to get your hands on
