Senior Data Engineer

Age of Learning is the leading developer of engaging and effective Pre-K through 5th grade learning resources. As a Senior Data Engineer on the Data and Analytics team, you will set the architectural direction for the data platform and ensure the reliability of the data layer, collaborating with various stakeholders and mentoring team members.

Responsibilities:

• Design, build, and maintain a simple, effective, and scalable data warehouse on Snowflake — with clean models, well-named fields, and documentation that makes the warehouse easy to use by downstream users and systems
• Implement and manage data transformation with dbt to ensure reliable, well-tested pipelines
• Develop and evolve data models, semantic layers, and metric definitions that support a wide range of business needs while keeping data consistent and accurate across the organization
• Own data quality, observability, and testing that protects downstream consumers from broken or misleading data
• Build and evolve internal AI tooling (skills, agents, etc) that makes the Data Engineering team more effective
• Partner with analysts, product engineers, and business stakeholders to understand their needs, scope the right solution, and deliver outcomes
• Mentor analysts and peers, fostering a culture of learning, rigor, and continuous improvement
• Proactively identify operational issues and propose evolutionary solutions

Requirements:

• 5+ years of data engineering experience, with a track record of owning systems end-to-end
• Strong SQL, Python, and data modeling skills — opinionated about design strategies and best practices
• Hands-on experience with dbt and Snowflake
• Experience with clickstream / event data
• Demonstrated ability to design and ship scalable data systems
• Comfort using AI tools (Claude Code, Cursor, or similar) as part of your daily workflow
• Excellent written communication — clear documentation, well-scoped specs, and the ability to explain technical tradeoffs to non-technical partners
• Strong project ownership: defining requirements as you go, communicating tradeoffs, and delivering results within timelines
• Ability to leverage abstraction to solve complex problems
• Experience designing semantic layers, metric stores, or data contracts
• Experience building A/B testing or experimentation frameworks
• Experience building or contributing to internal AI tooling — skills, agents