About the Role

We are seeking an experienced Business and Data Analyst to join our team. In this role, you'll be the analytical engine between a client's business need and the data solution that delivers it. You'll turn business questions into precise, testable requirements, then use the data itself to prove out what's possible: profiling sources, validating assumptions, and shaping designs that are clean, buildable, and grounded in evidence rather than opinion. You'll own the requirements and analysis for your use cases and workstreams, working in close partnership with Business Solution Managers who hold the broader engagement.

Doing that well takes two things at once: command of healthcare business context, and deep, hands-on fluency in the data. This is a client-facing role. You'll translate complex healthcare challenges into rigorous requirements, and you'll use SQL and analytical judgment to show clients and delivery teams what the data actually says, where it falls short, and what that means for the design.

No engagement lives in isolation. A data model built for one client's use case can share structure with a completely different client's need; a transformation rule written for one state's requirements often anticipates the next state's. You'll learn to see each requirement as part of a larger system of clients, states, use cases, and data flows, and to flag where a design can be reused or where it could collide with other work.

Our clients are US health plans and the partners who serve them, and the engagements span everything it takes to make their data usable. That can mean standardizing and migrating data onto a new platform, meeting evolving regulatory and compliance mandates, or powering the broader analytics and reporting programs that drive payer performance across Medicare, Medicaid, and Commercial lines of business.

Your day to day

Analyze the Data and Build the Evidence

• Write and execute SQL daily to profile source data, test assumptions, quantify data quality (completeness, validity, uniqueness, consistency, timeliness), and prototype logic before it reaches the build team.
• Analyze source data to establish what is actually available and reliable: field population and distributions, key integrity, duplicates, outliers, and gaps against what the business use case needs.
• Reconcile and validate outputs against source systems and client-provided benchmarks (claim counts, member months, paid amounts, enrollment spans, provider rosters), investigating variances to root cause and documenting findings.
• Define and document business metrics and KPIs precisely: numerator, denominator, inclusion and exclusion logic, time basis, and grain, so every consumer of the data calculates and reads them the same way.
• Perform exploratory and root-cause analysis on client questions and data anomalies, and turn findings into clear, quantified recommendations for client and internal audiences.
• Summarize findings through data profiling reports, analysis readouts, and visualizations that make the story clear to non-technical stakeholders.

Drive Requirements and Design for Your Use Cases

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Be a Trusted Client-Facing Partner

• Drive requirements-gathering and clarification conversations with client SMEs for your use cases, since incomplete or poorly elicited requirements undermine everything built downstream.
• Present analysis results, data quality findings, and recommendations to client stakeholders with clarity and confidence, and guide decisions with evidence.
• 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.
• Translate fluently between business and technical audiences, so both sides share the same understanding.

Collaborate Across Delivery

• 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.
• 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.
• 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.

What you bring to the team

• Bachelor's degree, or equivalent relevant experience.
• 5+ years working in the US healthcare domain and with healthcare data (claims, provider, membership/eligibility, benefits, prior authorization, or PBM).
• 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.
• 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.
• 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.
• Experience defining metrics, mentoring junior analysts, and eliciting and authoring business requirements in complex, data-driven environments.
• Confident client-facing communication; able to drive a conversation, elicit requirements clearly, and present analytical findings to technical and non-technical audiences.
• Comfort operating with ambiguity and re-prioritizing frequently while managing multiple use cases within a larger engagement.
• 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.

What we would like to see, but not required

• Exposure to Databricks or Snowflake
• 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.

Compensation

Compensation for this role is based on experience, skills, and location, and includes base salary plus eligibility for performance bonuses and equity grants.

What you’ll get in return

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Work arrangements

• Standard hours: 9 hours/day, 5 days/week
• Location: Pune, Onsite (1 PM - 10 PM IST)
• Shift: Your standard working hours will be nine (9) hours per day within the Company’s standard working hours. Specific working hours may vary based on business needs.