Lead Data Analyst, Data Products

Requirements

• Experience in ABA, autism services, or behavioral health analytics.
• Experience with value-based care, alternative payment models, or population health analytics
• Experience contributing to externally facing data products (benchmarking, payer intelligence, real-world evidence)
• Experience with modern data platforms (Snowflake, Databricks, BigQuery) and semantic-layer tools (dbt, LookML)

Benefits

Additional Information

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. CentralReach's Data Products team is seeking a Lead Data Analyst to be the analytical backbone of our group, reporting directly to the VP of Data Products. You will know our data model better than anyone, build the reusable analytical structures the rest of the company depends on, and personally take on the highest-stakes questions: the ones where the answer drives a real decision. The Data Products team is a startup operating inside CentralReach. We are establishing our own playbook and processes as we build. We are looking for someone with low patience for heavy process and high ownership mentality. You move fast, you say what you actually believe about what the data says, and you don't need someone telling you what the next question is. We believe AI tools are changing what great analytical work looks like, and we want someone who believes that too. Not as a productivity bonus, but as a fundamentally different way to move from question to defensible answer. Key Accountabilities: Become the internal expert on the CentralReach data model. Know how the tables relate, where they break, and what they can and cannot answer Design and build reusable analytical structures (entities, cohorts, episodes, analysis-ready datasets) that downstream work depends on Set the conventions for naming, versioning, lineage, and documentation so analytical work is reusable, auditable, and trusted across the company Partner with data product managers and with BI engineers on the handoff to product-facing measures Turn ambiguous business questions into testable analyses with clear assumptions, defensible methodology, and reproducible results Present findings so the so-what is clear and the methodology is auditable. Connect your work to outcomes, including the decisions you informed and the dollars, customers, or clinical results they moved Work with data product managers to identify gaps in the underlying data model and partner to fix them at the source Set the standard for how analytical work is done on the team: documented assumptions, reproducible code, version control, peer review Use AI tooling (LLMs, code copilots, semantic search) as a core part of your workflow. This is how the team works, not a nice-to-have. xperience 8+ years of hands-on healthcare data analytics experience in claims, clinical, or behavioral health data Expert SQL. Can write and tune complex queries at scale, design schemas for analytical use, and review someone else's code and know whether to trust it Demonstrated experience building analytical structures other people consume (marts, semantic layers, reusable cohort or measure definitions) Track record of analyses that drove real decisions. You can name the decision and the dollar, customer, or clinical impact Working fluency with at least one BI tool (Tableau, Power BI, Looker, Sisense, Metabase, or similar) and one analytical language beyond SQL (Python preferred) Strong writer and communicator. Can explain complex methodology to a non-technical executive without losing precision Comfort operating with significant autonomy on open-ended questions Active interest in using AI tooling as a core part of how you work

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