Senior Business and Data Analyst

Join our team as a Senior Business and Data Analyst, where you'll be the analytical engine connecting business needs to data solutions. You'll translate complex healthcare challenges into precise requirements, use SQL for data analysis, and work closely with Business Solution Managers. This client-facing role requires a strong command of healthcare business context and deep fluency in data.

- Translate complex healthcare challenges into rigorous requirements, and use SQL and analytical judgment to show clients and delivery teams what the data actually says. - Write and execute SQL daily to profile source data, test assumptions, quantify data quality, and prototype logic before it reaches the build team. - Lead discovery sessions for your use cases, and support broader engagement-level sessions alongside the Business Solution Manager, to understand the why and what behind each use case.

- Translate each use case into clear, testable requirements, including success criteria and non-functional requirements, and maintain traceability from business need to requirement to design to test - Document source-to-target mappings, transformation logic, and business rules that developers can build from, and define the test conditions and expected results QA will use to validate them - Use data analysis to sharpen and challenge requirements: confirm that what the client is asking for is supported by the data they have, and surface gaps early - Apply systems thinking: recognize when a design built for one client, state, or use case can become a reusable pattern, and raise and shape that reuse rather than discovering it in hindsight - Lead discovery sessions for your use cases, and support broader engagement-level sessions alongside the Business Solution Manager, to understand the why and what behind each use case: the business context, pain points, expected outcomes, and success criteria, rather than just the surface-level data ask - Recommend the appropriate grain and structure of a dataset given the business context, and analyze how that grain decision ripples into the downstream reports, models, or programs that consume the data - Identify dependencies before a design is finalized: which other workstreams, reports, or client use cases touch this same data, and how a change here could break or benefit them, escalating cross-workstream impacts to the Business Solution Manager - Present analysis results, data quality findings, and recommendations to client stakeholders with clarity and confidence, and guide decisions with evidence - Drive requirements-gathering and clarification conversations with client SMEs for your use cases, since incomplete or poorly elicited requirements undermine everything built downstream - Translate fluently between business and technical audiences, so both sides share the same understanding - Represent client intent for your use cases through delivery and serve as a day-to-day point of contact, with the Business Solution Manager owning the overall client relationship - Maintain awareness of the wider engagement: understand how a decision made for one workstream affects the others, and flag those ripple effects before they surface as rework - Support QA and UAT by supplying validation queries, expected results, and variance analysis, and by helping triage defects to business rule, mapping, or source data causes - Connect the client and the internal team on your workstreams, holding the business context, data findings, and design drivers that shape the build - Act as the business and data analysis SME for Solution Architects, Product, QA, Account Management, and Program Management, giving them clean hand-offs with the context, business rules, and analysis they need to execute without rework or reinterpretation - Strong, hands-on SQL: comfortable with joins across large, multi-table datasets, window functions, CTEs, aggregations, and date/span logic, used regularly to profile, validate, reconcile, and analyze data. Exposure to Databricks or Snowflake is desirable. This is distinct from a software or data engineering background - Confident client-facing communication; able to drive a conversation, elicit requirements clearly, and present analytical findings to technical and non-technical audiences - 5+ years working in the US healthcare domain and with healthcare data (claims, provider, membership/eligibility, benefits, prior authorization, or PBM) - Experience defining metrics, mentoring junior analysts, and eliciting and authoring business requirements in complex, data-driven environments - Demonstrated analytical rigor: a track record of profiling data, assessing data quality, investigating anomalies to root cause, and communicating quantified findings that drove a decision - Comfort operating with ambiguity and re-prioritizing frequently while managing multiple use cases within a larger engagement - Bachelor's degree, or equivalent relevant experience - Payer experience required: direct experience working with or for a US health plan (Medicare, Medicaid, or Commercial), whether in-house or through healthcare consulting - Working command of core payer concepts such as membership/enrollment, provider, claims, and authorizations, enough to reason about business context rather than specialize narrowly in any one area - Developing systems thinking: experience designing for reuse and scale across states, clients, or use cases rather than solving each case in isolation, and the ability to trace how a requirement or data model decision affects other parts of a larger data ecosystem - Background that already blends business analysis with hands-on data work, such as a Business Analyst, Data Analyst, or Business Data Analyst role, with exposure to client-facing delivery - Experience with BI and visualization tools (e.g., Tableau, Power BI, or similar) to communicate analysis - Familiarity with HEDIS and Stars quality reporting, risk adjustment, payment integrity, value-based care analytics, provider data management, or CMS interoperability (CMS-0057) - Working knowledge of data modeling concepts, including how the grain and structure of a dataset shape its downstream use - Familiarity with Python or similar for analysis and automation - Familiarity with Medicaid data or PBM platforms