Principal Software Engineer, Health Data & Interoperability (Remote)

Verto Health

4d•$165,000 - $190,000•Remote

About The Position

Verto Health is a health technology company focused on solving complex data fragmentation and interoperability challenges in healthcare. Their AI-enabled platform unifies clinical data from various sources (EHRs, claims, payer systems) to provide a comprehensive view of patient populations. Verto collaborates with health systems and value-based care organizations to transform fragmented data into personalized patient journeys, automated workflows, and accurate risk adjustment, ultimately enabling providers to reduce manual work, enhance operational efficiency, and deliver proactive, data-driven care at scale. As a Principal Software Engineer, Health Data & Interoperability, you will be a senior technical leader responsible for the product's data architecture. Your role will involve setting high standards for code quality, developing an AI-assisted development pipeline, and owning the canonical data model and its associated pipelines. This is a hands-on position requiring you to write and review code, and design the ingestion and transformation processes for diverse healthcare data (HL7 v2, FHIR, claims, third-party sources) into a usable format for the product. Reporting to the CIO, you will collaborate with engineering, AI engineering, product and delivery, and implementations teams.

Requirements

Bachelor's degree in Computer Science, Engineering, Health Informatics, or a related field, or equivalent experience.
7+ years in software and data engineering, including 4+ years hands-on building and operating production systems on a team that reviewed each other's code.
Strong current Python and SQL, with excellent software engineering craft. You will be writing code and reviewing everyone else's.
Hands-on HL7 v2 and FHIR experience with real EHR and claims data, including designing the ingestion behind it. You have debugged live feeds, not only read the specifications.
Experience designing canonical data models, schemas, and ETL at scale across multiple source systems, rather than a single integration.
You’ve set and held a code review bar on a team, and are comfortable leading through influence without direct reports.
Hands-on with AI-assisted development tooling, with an interest in building the harnesses rather than only using them.
Cloud data platform experience, ideally Azure, and working knowledge of PHIPA, PIPEDA, or HIPAA as they bear on health data.
A strong communicator who can align a room on a complex system and explain it just as well to a client executive as to an engineer.
At ease finding the simplest workable route through a messy integration, in a company where scope is broad and resources are lean.

Nice To Haves

Experience with interface engines such as Mirth Connect, Rhapsody, or InterSystems, and standards including HL7 v3, C-CDA, IHE, DICOM, or X12 claims files such as CCLF.
Experience with clinical terminologies including SNOMED CT, LOINC, and ICD-10, or analytical modelling such as OMOP CDM.
Familiarity with the Canadian digital health landscape, including Ontario Health, provincial health information exchanges, or Canada Health Infoway.

Responsibilities

Set and hold the code review bar, and raise the standard of what ships rather than only reviewing what arrives.
Build and drive our AI-assisted development pipeline, from the agentic harnesses themselves through to daily use of frontier coding tools across the team.
Own technical R&D and spikes: evaluate what is worth adopting, prove it, and bring back something the team can use.
Hold the line on technical debt and resilience, including retiring legacy code and fixing projects that only one person understands.
Own the canonical healthcare data model covering patients, encounters, providers, claims, and clinical events, fit for longitudinal records, cross-source analytics, and EMPI.
Own the semantic terminology service, the platform APIs applications consume, and the datasets the application and analytics layers depend on.
Set the standards for data quality, lineage, and standardization, and build provenance in outputs informing a clinical or financial decision can be traced to source.
Own decision rights on the data model and data standards, and make the data-side calls in the Architecture Review Board.
Design the ingestion and ETL that brings HL7 v2, FHIR, claims, and third-party data into the platform, and keep it working as sources change.
Work with federated query engines such as Trino and with ingestion patterns that span many source systems.
Optimize pipelines for throughput, cost, reliability, and latency, and own root cause when a feed breaks.
Build for operability so the engineering team can run what you design without you in the loop.
Keep ingestion patterns reusable across clients rather than rebuilding them for each client.