CentralReach is a leading provider of autism and IDD care software for Applied Behavior Analysis (ABA), multidisciplinary therapy, and special education. Trusted by more than 200,000 users, we enable therapy providers, educators, and employers to scale the way they deliver ABA and related therapies with innovative technology, market-leading industry expertise, and world-class customer satisfaction.
As a Technical Product Analyst at CentralReach, you will sit within the Data Product teams, supporting Product Managers by providing the analytical and technical depth needed to translate requirements into validated, production-ready data products. You will bridge the gap between product strategy and engineering execution, ensuring that the data logic underlying our analytics offerings is as sound as the features built on top of it.
You will work closely with SQL-based data validation, requirements refinement, and process improvement, while also serving as a front-line resource for internal and external stakeholders with questions or issues related to data and dashboards. This is a hybrid role, part product execution, part data QA/validation, part process improvement, suited to someone who is comfortable owning ambiguity in data logic and wants to grow their product and analytical skill set within a fast-moving healthcare data organization.
Responsibilities:
- Partner with the ABI and DW Product Managers to refine requirements, define acceptance criteria, and support backlog grooming and release readiness.
- Own SQL-based data validation and metric reconciliation, verifying source-to-target data logic and data transformation into the elasticubes.
- Direct and interpret output from AI-agent QA tooling, designing test cases and following up on discrepancies.
- Investigate reported data discrepancies and production issues, distinguishing upstream pipeline problems from BI-layer or presentation issues, and escalate to Engineering when appropriate.
- Serve as the front-line owner for internal and external user questions on data and dashboards, triaging and resolving requests or routing them to the right team.
- Support regression testing across releases, ensuring prior fixes and validated logic remain intact as new features and data sources are introduced.
- Turn recurring manual checks into reusable, documented validation rules, and identify opportunities to automate QA and data-quality workflows.
- Conduct market, competitive, and data-driven research on an ad hoc basis to support PM decision-making.
