Analytics Engineer
About the role
Role overview
Merit America is seeking an Analytics Engineer to own the analytics platform that powers reporting, analysis, and decision-making across the organization. We're looking for someone who treats this as their platform: someone who takes responsibility for the data models, semantic layer, and reporting standards that teams rely on, and who proactively makes them more trustworthy, scalable, and easier to use rather than waiting for work to be assigned.
Our stack runs on BigQuery, dbt, Lightdash, and Fivetran. The core of the role is transforming raw source-system data into canonical models, governed metrics, and self-service reporting that teams across the organization can trust. Much of the challenge lies in the business domain itself: learner lifecycle, funnel, and outcomes definitions evolve as programs change, so judgment, curiosity, and strong communication matter as much as technical skill. We are also exploring how AI can improve how we build, maintain, and use our analytics platform, and are looking for someone who is excited to experiment thoughtfully with these tools.
This is a hands-on, individual-contributor role reporting to the Head of Data & Technology and working closely with data analysts, software engineers, and stakeholders across Program, Growth, Finance, and Operations.
Responsibilities
The responsibilities of the Analytics Engineer will include, but are not limited to, the following:
Own and improve canonical data models
- Own and evolve the dbt models that transform raw source-system data into durable, reporting-ready tables.
- Improve models over time by reducing duplication, clarifying grain, and documenting business logic.
- Turn recurring reporting needs into reusable models rather than one-off queries.
- Identify opportunities to simplify the data model and make reporting easier to understand and maintain.
Govern metrics and the semantic layer
- Own the Lightdash semantic layer so core metrics are defined consistently and documented where they're used.
- Maintain source-of-truth definitions, promoting and deprecating metrics as the business changes.
- Improve the reporting ecosystem over time by making analytics more self-service and easier to use.