Principal Solutions Engineer, Data & AI

About this role

At MedScout, our mission is to empower MedTech commercial teams with the data, insights, and tools they need to deliver life-changing medical innovations to the patients who need them most. We’re creating a best-in-class revenue acceleration platform that unites the latest medical claims intelligence with an intuitive user experience built specifically for sales professionals at medical device and diagnostic companies.

We've raised $31.8M from incredible investors to redefine this category at a fast pace. To keep up with demand, we're looking for a founding solutions engineer: technical enough to architect and validate data solutions, commercial enough to create the art of the possible, and ready to own the technical outcome of our largest enterprise deals.

What the Role is…

This is the founding Solutions Engineering role. We are hiring a senior, hands-on solutions leader to serve as the technical and data expert in our most important enterprise sales cycles. This is not a traditional demo-support role. You will help customers understand how MedScout fits into their data and insights strategy, build tailored demo and proof-of-value experiences, lead claims-data and integration discussions, and show what is possible as our AI capabilities evolve.

You will work closely with Sales, Product, Data, and Engineering and will help turn the deep industry knowledge held by MedScout's commercial leadership into repeatable tools, stories, and best practices for the broader team.

What You Will Do…

- Lead technical discovery and solution design. You diagnose before you defend. Working across analytics, data engineering, IT, security, commercial ops, and executive stakeholders, surface the commercial questions the customer is trying to answer (where procedures are happening, which providers matter, where referrals leak) and map the environment those answers land in: CRM, warehouse, identity spine, incumbent vendors. You leave discovery able to name the gap between their data today and what their strategy needs, and to turn that into clear requirements, delivery method, and architecture.

- Turn our data into commercial insight. The data team owns how the data is built. You own what it means to a buyer. Coverage, latency, identity resolution, attribution, and code set logic come up live in technical evaluation, including longitudinal patient journey questions where you scope precisely what the data can and cannot answer, then move the room to the answer they came for.

- Own data delivery into customer environments. Stand up repeatable motions for scoping and delivering Snowflake Secure Data Sharing, Databricks Delta Sharing, cloud object storage, API delivery, CRM, and downstream analytics workflows.

- Own the RFP and security review. The technical, methodology, and governance sections of every RFP, RFI, and questionnaire are yours, including SOC 2, HIPAA, BAAs, and data use agreements. You orchestrate data, engineering, product, and legal, and you own quality and turnaround.

- Enable the demo environment. Fresh data templates, working accounts, a defined reset cadence, persona-specific paths for the sales leader, analytics buyer, data evaluator, and executive sponsor(s), and packaged plays for our core use cases: territory optimization, competitive displacement, referral leakage, site of care shift, patient journey, and launch targeting.

- Own the technical narrative for AI. Our in-product agent, our public MCP server, and what customers build when their commercial intelligence reaches their own AI tools. Answer the provenance, grounding, access control, and governance questions, so AI stops being a deal blocker. Track how the AI tool ecosystem is moving and translate it into what a specific customer should actually build with our data.

- Institutionalize what you learn. Convert tribal knowledge into discovery guides, training materials, answer libraries, architecture patterns, and enablement, and run the feedback loop from the field back to Product.

What We Are Looking For…

- 5+ years in a customer-facing technical role. Solutions engineer, sales engineer, solutions architect, solutions consultant, or heavily client facing data consulting. Title matters less than scope: you have personally owned the technical outcome of enterprise deals.

- Hands on Snowflake, Databricks, or an equivalent platform. You have worked with a modern Data Warehouse. You have written and tuned SQL against very large tables, you understand warehouse cost and performance, and you have set up or consumed secure data shares. You will be asked to demonstrate this. Bonus points if you’ve built apps on Streamlit in Snowflake (SiS) or Databricks Apps, or if you’ve used modern BI tools like Tableau, Hex, Superset, etc.

- Real integration experience. You have delivered data into someone else's environment by warehouse share, API, or file, and survived the security review and the data use agreement that came with it. You can talk credibly about REST API design, authentication, pagination, rate limits, and webhooks with a customer's engineering team.

- CRM fluency. You can speak to managed packages, object models, field mapping, and sync direction, and advise a customer on what to bring into Salesforce, HubSpot, or Dynamics, and what to leave in the warehouse.

- Healthcare claims or real world data fluency. Earned on the buy side, in a health system, or at a vendor, all count equally. Working command of CPT, HCPCS, ICD-10, PLA, DRG, place of service, NPI and taxonomy, and site of care logic.

- Command of the room, and clear writing. You can hold your own with a data engineer and a VP of Sales back to back, and you know when the room needs the methodology and when it needs the punchline. A large part of this job is writing.

- Daily working use of AI tools. Claude, ChatGPT, Copilot, Cursor, or similar, so you can speak credibly to a customer about agentic workflows because you build with them.

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