Senior Finance Data Analyst - Healthcare

Job Description

About AbarVa

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AbarVa is building Nexus, an AI-native enterprise execution platform that helps organizations turn strategic decisions into governed action, measurable outcomes, and sustained business value.

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We are building Nexus alongside complex enterprise Data & AI transformations, particularly in regulated industries. Our teams combine business context, data engineering, analytics and AI to help organizations modernize how data is built, governed and used for decisions.

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The opportunity

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We are looking for an experienced Senior Finance Data Analyst / Consultant – Healthcare to join AbarVa’s Data & AI team.

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This is a client-facing analysis and design role at the intersection of healthcare finance, analytics and data engineering. You will work directly with Finance, FP&A, Actuarial, Medical Economics, Analytics and Engineering teams to understand how important financial measures and reports are produced today, trace the underlying data and business rules, identify gaps, and help translate that knowledge into governed data products on a modern cloud data platform.

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We are not looking for someone who simply gathers requirements and hands them to engineering. We are looking for someone who can get into the details, follow a number back to its source, understand why it is calculated the way it is, reconcile it, document it clearly, and work side-by-side with engineers to make sure the new data product is both technically sound and financially correct.

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What you will do

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• Lead and support current-state analysis and design sessions with finance, actuarial, analytics and technology stakeholders

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• Understand and document existing financial reporting processes, data flows, business rules, calculations, reconciliations, spreadsheets, reports and manual workarounds

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• Trace key financial and healthcare measures from business reports back to source systems and underlying data

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• Define clear business and data requirements, including grain, calculation logic, source-of-record, refresh requirements, ownership and acceptance criteria

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• Develop source-to-target mappings and work with Data Engineers on Bronze, Silver and Gold data-product designs

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• Profile claims, membership, eligibility, financial and related healthcare datasets to identify quality, completeness and reconciliation issues

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• Define data-quality rules, financial control totals and reconciliation requirements

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• Compare new-platform outputs with existing reports and source systems, investigate variances and drive issues to resolution

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• Support business UAT and help establish the evidence required for business owners to trust and approve new data products

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• Work with BI and analytics teams to shape dashboards, financial analytics and self-service capabilities on top of governed data

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• Help rationalize legacy reports and measures—identifying what should be retained, redesigned, consolidated or retired

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• Partner closely with Data Engineering, Databricks, BI and AI teams throughout delivery rather than operating as a separate requirements function

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Mandatory domain expertise

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• Strong healthcare payer / managed-care domain experience, including hands-on knowledge of claims, membership and eligibility, capitation, medical cost, PMPM, rate cells, actuarial or finance reporting, and how these measures are used by Finance and business teams

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• Demonstrated experience working with healthcare financial or actuarial data and reconciling complex business measures across source systems, reporting platforms and financial outputs; candidates without meaningful healthcare finance / payer data experience are unlikely to be a fit for this role

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What we are looking for

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• 7+ years of experience in healthcare finance, healthcare analytics, financial reporting, actuarial analytics, data consulting or a related field

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• Strong experience working directly with business stakeholders and subject-matter experts

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• Strong SQL and hands-on data-analysis skills

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• Ability to understand complex calculations and trace a financial measure across multiple source systems

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• Experience documenting business rules, mappings, metric definitions and reconciliation logic

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• Strong experience with financial reconciliation, controls, data validation or reporting accuracy

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• Ability to translate business requirements into specifications that Data Engineers can build from

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• Strong analytical problem-solving skills—you should be comfortable investigating why two numbers do not match and staying with the problem until you understand why

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• Excellent written and verbal communication skills

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• Ability to operate comfortably with both senior business leaders and deeply technical engineering teams

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Relevant healthcare experience may include

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• Claims and encounters

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• Membership and eligibility

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• Medicaid or Medicare

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• Health-plan finance

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• Actuarial reporting

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• IBNR and reserve development

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• Medical economics

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• Risk adjustment

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• Capitation and rate development

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• Regulatory reporting

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• Provider and network analytics

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Technology experience

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You do not need to be a Data Engineer, but you should be technically fluent enough to work closely with one.

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Experience with some of the following is helpful:

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• Databricks or another modern cloud data platform

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• AWS

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• SQL-based analytical data models

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• Tableau, Power BI or similar BI platforms

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• IBM Planning Analytics / TM1

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• Data-quality and reconciliation frameworks

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• Data lineage and governance

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• Modern data-product or medallion architecture concepts

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How we work

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AbarVa is building an AI-native delivery model.

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Our senior people define the problem, business rules, data contracts and engineering standards. AI-assisted engineering tools help accelerate implementation, testing and documentation. Engineers review and own what is built. Business owners validate the numbers and outcomes.

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For this role, AI does not replace financial or domain judgment.

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It makes having precise definitions, reconciliations, source lineage and clear acceptance criteria even more important.

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What makes this role different

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The question we want this person asking is not:

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“What report do you want?”

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It is:

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“Where did this number come from?”

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“What business rule produced it?”

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“Who owns the definition?”

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“What should it reconcile to?”

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“What decisions depend on it?”

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“And what data product should we build so the organization does not have to reconstruct it again next month?”

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If that is how you approach data and financial analytics, we would like to talk.