Merit America

Analytics Engineer

• 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.
• 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.
• 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, documentation, testing
• Review analytics work for modeling quality and maintainability, and help analysts use the stack effectively.
• Cross-train teammates so knowledge is shared rather than concentrated.

Revenue Operations

• Own the AI-enabled revenue forecast model. Integrate pipeline, capacity, and enrollment data across all channels to directly drive development team goal-setting and performance tracking.
• Synthesize inputs from program, finance, partnerships, and development teams into executive-ready projections and strategic decision materials for leadership.
• Monitor actuals versus forecasts, surfacing variances early with clear data-driven explanations and actionable next steps.
• Maintain executive dashboards to provide leadership with a real-time, transparent view of overall revenue health.
• Support the Managing Director on pricing and packaging analysis by executing data modeling and preparing high-stakes materials for leadership and partner conversations.
• Serve as the operational link between operations and finance, ensuring all revenue sources are modeled accurately in the forecast.
• Manage compliance requirements across all revenue sources—to ensure we are compliant with all revenue producing partnerships and funders.
• Monitor program completion rates against deliverables in philanthropic and corporate contracts, flagging variances early to protect funder and partner relationships.
• Partner with the Learner Financing Specialist and finance team to unify financing data, compliance status, and learner outcomes into a single, cohesive view of revenue health.
• Own day-to-day governance of Salesforce as the system of record for partner pipeline and revenue — enforcing data entry standards, not just using the tool
• Monitor funder/partner health indicators and flag at-risk relationships before they affect the forecast
• Support the grant renewal process by ensuring renewal timelines, contract terms, and partner status are tracked proactively and accurately
• Continuously improve the CRM as a forecasting input, identifying gaps between what the data says and what the organization actually knows so as to reduce toil for the Development and Partnership Teams.
• Coordinate cross-functionally to pressure-test learner volume, learner performance against program schedule and partnerships commitments.
• Prepare regular revenue reports and forecasting summaries that can be shared directly to leadership team.
• Translate complex analytical findings into clear, accessible insights for non-technical stakeholders across the organization.

Analytics Engineer

Manager, Revenue Operations