Xometry
Analytics Engineer II
About this role
Xometry seeks an Analytics Engineer II to design and maintain production-grade data models using dbt, serving as the transformation layer for enterprise reporting. You'll bridge data engineering and analytics, collaborating with cross-functional teams to turn raw data into clean, actionable datasets that drive organizational decision-making.
What you'll do
- Design, develop, and maintain robust dbt-based data models following semantic modeling and multi-domain architecture principles
- Partner with data engineers to optimize data ingestion pipelines, warehouse reliability, and query performance
- Translate complex business requirements from analysts and stakeholders into technical data specifications
- Lead implementation of version control, CI/CD patterns, and comprehensive data quality and unit testing
- Create and maintain high-quality data dictionaries and documentation to establish consistent data definitions across the organization
- Collaborate with data analysts to ensure models support reporting and analytical needs
What they're looking for
- dbt modeling and transformation
- Advanced SQL for complex data transformations
- Cloud data warehouse experience (Snowflake)
- Git version control
- Dashboard and BI tools (Looker, Tableau, or Power BI)
- Data quality testing and validation
- Semantic modeling and multi-domain architecture
- CI/CD patterns and practices
Opens the official application on the employer’s site. No login required.
Xometry
Xometry operates a manufacturing network platform that connects with suppliers and partners to deliver complex projects, particularly in regulated industries like aerospace and defense. The company is hiring analytics engineers to build data infrastructure, and supplier quality and development engineers to optimize partner performance, ensure product quality, and manage manufacturing relationships.
- Website
- xometry.com
Likely interview questions
- Walk us through a complex data model you've built in dbt—what was the business problem, and how did you structure it?
- How do you approach ensuring data quality and reliability in production pipelines?