Technical Product Analyst at CentralReach

CentralReach is a leading provider of autism and IDD care software for Applied Behavior Analysis (ABA), multidisciplinary therapy, and special education.

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.
• Maintain clear documentation of requirements, test cases, validation results, and known issues to build a durable artifact trail for both product teams.
• Communicate validation findings, risk areas, and open issues clearly to PMs, Engineering, and other stakeholders, including status updates during release cycles.

Desired Skills & Experience:

• 2+ years of experience in a data analyst, BI analyst, QA/data validation, or technical product analyst role, ideally in a healthcare or SaaS data environment.
• Strong proficiency in writing and reading SQL, with the ability to validate data against source-to-target logic rather than relying solely on BI-layer output.
• Hands-on experience with BI reporting and visualization tools (e.g., Sisense, Tableau, Power BI, Looker), including building and maintaining dashboards, is required.
• Familiarity with (or ability to quickly learn) elasticube structures and AI-agent tooling.
• Experience working within Agile/Scrum development processes, including requirements refinement, backlog support, and release readiness.
• Comfortable owning ambiguity in data logic without needing everything pre-specified, while knowing when to escalate to a PM or Engineering.
• Strong written documentation habits, with the ability to produce clear requirements, test cases, and QA artifacts.
• Detail-oriented with strong analytical and troubleshooting skills, able to reconcile discrepancies across multiple data sources.
• Excellent communication skills, with the ability to work effectively with both technical (Engineering) and non-technical (stakeholder, customer-facing) audiences.
• Python experience is preferred but not required.
• Prior exposure to healthcare data, EMRs, or ABA-specific data (billing, scheduling, authorization, clinical session, or clinical goals domains) is preferred but not required.
• Process-improvement mindset, with an interest in turning recurring manual work into reusable, automated validation rules.