Analytics Engineer

The Organization: Merit America The American economy is broken. Today, 53 million working adults–nearly half of the U.S. workforce–do not earn a living wage. These talented workers have few options to advance: college is too long and expensive, full-time boot camp programs don’t offer enough flexibility, and online courses don’t have the structure or support to translate learning into a new career. The result? Talented workers, disproportionately people of color and women, get stuck in low-wage roles with no way to build a better life for themselves and their families. Merit America is a national nonprofit that creates pathways to family-sustaining careers for Americans stuck in low-wage work. Our fast, flexible solutions are built for working adults: We start by analyzing tens of millions of job postings to identify in-demand, high-paying tech careers and then work with industry-recognized partners to train for these roles with part-time programs that combine flexible online learning with best-in-class coaching. Finally, we support our learners in their job search, helping them connect with a broad constellation of local and national employers such as JPMorgan Chase and Infosys to land higher-earning, family-sustaining jobs. Merit America is on a mission to build a scalable pathway for workers to join the middle class through merit, not money. Since our founding in 2018, we’ve generated a projected $1 billion in near-term wage gains for our learners. 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. Maintain data quality and platform reliability Build and maintain observability, testing, and monitoring across the analytics stack so data quality issues are identified early and resolved across source systems, transformations, and BI outputs. Maintain strong development practices: version control, PR review,…