• Partner directly with stakeholders across the business (Product Development, Marketing, DTC, B2B, Finance, Operations, Inventory Planning) to translate ambiguous questions into well-defined analyses, dashboards, and data products. You’ll own these end-to-end: scoping, building, validating, and communicating findings. • Build and maintain dbt models that turn raw source-system data into trustworthy, well-documented datasets. Write the tests and documentation that let both humans and AI agents downstream rely on the work. • Develop and maintain the semantic context, dashboards, and reports that the rest of the business uses to operate day-to-day. • Own metric definitions and business semantics. Drive alignment when stakeholders disagree on what a definition or number means. • Review and harden AI-generated SQL, dbt models, and Python code with the judgment to catch issues that pass tests but are semantically wrong. The majority of your output will be code you’ve collaborated on with AI agents, and you’ll bring the data intuition that makes that work trustworthy. • Investigate ambiguous data questions where the answer isn’t in the schema: talk to source-system owners, investigate edge cases, reconcile conflicting definitions, and improve our model of the business. • Help build and maintain Stio’s data infrastructure — currently Snowflake, Fivetran, dbt, GitHub, Power BI, R, and Python — and contribute to decisions about where the stack should evolve. • Improve data governance for both the Data & Analytics team and the business at large by creating documentation that’s actually useful and that AI agents can consume as context for future work. • Continuously develop your skills as the practice of data analytics evolves. This is a real part of the job, not something done on the side.

• Partner with business stakeholders to gather, clarify, and document reporting and analytics requirements. • Work closely with BI Developers, BI Architects, Data Architects, and Data Engineers to translate business needs into technical specifications. • Understand where data resides across source systems, data warehouses, semantic layers, and BI tools. • Analyze ETL/ELT processes, data flows, transformations, and dependencies to support BI solution design. • Document source-to-target mappings, business rules, metric definitions, filter logic, report logic, and reporting assumptions. • Create clear, development-ready user stories, tickets, acceptance criteria, and supporting documentation for technical teams. • Support backlog refinement, sprint planning, testing, and release coordination for BI and analytics work. • Validate reporting outputs against source data, business rules, requirements, and expected results. • Investigate data discrepancies, reporting defects, and gaps between business expectations and technical implementation. • Coordinate across BI, data architecture, data engineering, and business teams to resolve issues and drive delivery. • Support standardization of BI requirements, documentation practices, reusable reporting patterns, and intake processes. • Assist with user acceptance testing and production validation for new or enhanced dashboards, reports, datasets, and semantic models. • Communicate technical concepts clearly to non-technical stakeholders and communicate business context clearly to technical teams. • Identify opportunities to improve reporting processes, data usability, development handoffs, and operational efficiency.

• Gather and document functional and technical requirements • Coordinate with SMEs to understand data sources, business rules, data processes, and analytic needs • Conduct data validation, interpret outputs, and reconcile discrepancies with data engineers and dashboard developers • Support sprint planning, backlog development, requirement reviews, and cross‑team coordination • Support data modeling and dataset definition discussions • Support User Acceptance Testing (UAT) for new dashboard features and enhancements • Support the current projects by tracking relevant data and dashboard metrics, developing and updating reports, providing administrative and document related support • Coordinate across the project team and other internal stakeholders as well as with subcontractors as needed • Develop, propose, and implement solutions for material or logistics related issues to maintain program and project goals.

• Build, maintain, and govern payer and plan data across workflow and pricing systems. • Configure and manage submission addresses and methods. • Standardize payer and plan names, aliases, and identifiers. • Analyze and resolve routing exceptions and data discrepancies. • Audit payer master data to ensure accuracy, completeness, and integrity.