Job Description

The problem we’re solving: everyone has data, but nobody trusts the same number. Clinical ops, finance, HR, and revenue cycle teams each have their own spreadsheets, their own definitions, and their own version of the truth. We’re building the layer that fixes that.

As our Data Products Architect, you will be the person who sits between the business and the data platform and makes the translation work. You’ll go deep with operational leaders to understand how their processes actually run (not how they’re documented), then architect the semantic models, metric definitions, and data products that turn that knowledge into trusted, governed, reusable analytical assets in our modern lakehouse.

This is not a “build dashboards from tickets” role. You will own the business logic layer: deciding how we define a “billable hour,” reconciling why payroll and finance see different headcounts, and designing the data contracts that let every team work from one source of truth.

Responsibilities

Job Duties and Responsibilities include the following. Other duties may be assigned.

Translate business processes into data products

• Partner with leaders across clinical operations, intake, scheduling, payroll, finance/FP&A, HR, and revenue cycle to map end-to-end workflows as they actually operate.
• Identify key decision points, handoffs, bottlenecks, SLAs, and operational risks embedded in those processes.
• Convert process knowledge into data requirements, metric definitions, and reusable data products: facts, dimensions, views, aggregates, scorecards, alerts, and semantic datasets.

Bridge source systems to business outcomes

• Trace business events and entities across systems including EHR/clinical platforms, CRM, HRIS/payroll, finance systems, and collaboration tools.
• Define source-to-target mappings and transformation logic for our lakehouse (Bronze/Silver/Gold architecture).
• Resolve common healthcare and operational data problems: inconsistent identifiers, evolving business rules, stage/status mismatches, late-arriving data, and historical restatements.

Architect trustworthy analytics products

• Design and document data products with clear ownership, purpose, definitions, assumptions, and acceptance criteria.
• Partner with data engineering to implement scalable dimensional models, marts, and views aligned to actual business use.
• Ensure products are consumable by BI tools, spreadsheets, operational teams, and AI/agent workflows.

Enable Adoption and Drive Stakeholder Value

• Work directly with consumers of data products to ensure they understand what they’re looking at and trust the outputs.
• Train operational and analytical users on self-service access to governed data products.
• Continuously gather feedback and iterate on data product design to increase coverage and accuracy

Qualifications

Qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Education/Experience: Bachelor's degree from four-year college or university; or one to two years related experience and/or training; or equivalent combination of education and experience.

Skills:

• Business process fluency: Demonstrated ability to rapidly learn complex, multi-stakeholder business processes, identify value creation levers, and translate operational knowledge into structured data requirements. You should be as comfortable mapping a revenue cycle workflow as you are writing a SQL query
• Stakeholder partnership: Proven track record of working directly with senior leaders (VP, C-suite, board-level) and line-level operators to define problems, align on priorities, and drive adoption of new tools and processes. You don’t wait for requirements; you go find them.
• Ownership and accountability: You take initiatives from problem identification through implementation, adoption, and iteration. You are the primary change agent, not a supporting analyst. You are accountable for whether your data products actually move the business.
• Structured problem-solving: Ability to decompose ambiguous, cross-functional problems into workable components, prioritize by impact, and drive to resolution. Experience with hypothesis-driven analysis and rapid prototyping of solutions.
• Communication and influence: Exceptional ability to communicate complex data concepts to non-technical audiences and build consensus across functions. You write clearly, present confidently, and know when to push and when to listen.
• PE/growth environment readiness: Comfort operating in a PE-backed, metrics-driven, high-accountability environment. You understand that the work you do needs to tie to enterprise value, not just analytical elegance.
• Healthcare domain fluency: Experience navigating healthcare data environments including clinical data structures, payer/billing workflows, authorization cycles, and PHI/PII handling under HIPAA. You don't need to be a clinician, but you need to understand why healthcare data is uniquely messy and how compliance constraints shape what you can build and how you build it.
• AI-augmented operating style You actively use AI tools (Claude, Gemini, Copilot, etc.) to accelerate your work — whether that's drafting transformation logic, pressure-testing metric definitions, rapid-prototyping data models, or generating documentation. This isn't about prompt engineering as a skill; it's about the instinct to use every available lever to move faster and think more clearly.

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