About the Company
Verto Health is a health technology company solving some of healthcare’s most complex data fragmentation and interoperability challenges. Our patented, AI-enabled platform brings together structured and unstructured clinical data across disparate EHRs, claims, and payer systems to create a unified, actionable view of patient populations.
We work with health systems and value-based care organizations across North America to turn fragmented healthcare data into personalized patient journeys, automated workflows, and more accurate risk adjustment. By connecting data to action, Verto helps healthcare providers reduce manual work, improve operational efficiency, and deliver more proactive, data-driven care at scale.
About the Role
As Principal Software Engineer, Health Data & Interoperability, you will be the senior technical voice on our engineering team and the owner of the data architecture underneath the product. You’ll set the bar for what good code looks like, build the AI-assisted development pipeline the team works through, and own the canonical model and the pipelines that feed it.
This is a hands-on role. You'll write code, review everyone else's, and design the ingestion and transformation that turns fragmented and inconsistent healthcare data across HL7 v2, FHIR, claims, and third-party sources into something the product can depend on.
Reporting into the CIO, you'll largely interface with four teams. The engineering team owns application architecture and software development, and will operate the pipelines you design. The AI engineering team owns AI system design and evaluation, and will depend on you for reliable modelled inputs. The product and delivery team owns the roadmap. The implementations team owns client projects and integration delivery.
What You'll Do
Engineering Leadership & R&D
- 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.
Data Architecture
- 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.
Interoperability & Data Pipelines
- 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.
What You Bring
Knowledge & Experience
- 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.
Preferred
- 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.
How You Work
- 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.
What Success Looks Like
- Pipelines that hold: ingestion keeps working as sources change, and cost and latency improve rather than drift.
- The bar rises: code quality, review standards, and AI-assisted development adoption visibly improve across the team.
- Architecture that holds: the canonical model and semantic layer absorb new clients and sources without a redesign each time.
- Data trust: quality, lineage, and standardization standards are met and demonstrable, with audit evidence ready when asked.
- Knowledge shared: architecture decisions are documented and technical depth is growing in the team, not concentrated in one person.
Where You'll Work
- This role is remote and open to candidates anywhere in Canada.
- We hold in-person team days at our downtown Toronto office roughly once a quarter.
Why Verto
- Own the data foundation everything we sell depends on, in a product used by health systems across North America
- Genuine architectural ownership without leaving the code
- Shape how AI-assisted engineering is practiced across the team
- Work on some of the hardest data interoperability problems in healthcare, with the autonomy to actually solve them
- Generous professional development support and flexible work options
Compensation
The salary range for this role is $165,000 to $190,000 CAD per year, depending on experience, skills, and alignment with the role.
In addition to base compensation, Verto Health offers a comprehensive benefits package, which may include health and dental coverage, paid time off, professional development support, and flexible work arrangements.
Compensation is reviewed regularly to ensure fairness, market alignment, and internal equity.
Ready to Apply
If you’re eager to tackle some of healthcare’s hardest data problems, own the architecture that turns fragmented data into something teams can actually build on, and shape how AI-assisted engineering works across a growing team, we’d love to hear from you.
Do you have 70% of the qualifications we're looking for? That's enough. Apply anyway.
If you have come across this post through an external job board, we strongly encourage candidates to apply directly through our careers page here.
Verto Health is committed to providing an inclusive and accessible experience for all applicants. If you require accommodations during the recruitment process, please let us know and we'll work with you to meet your needs.
Use of AI in the Hiring Process
Verto Health uses artificial intelligence-enabled tools during the recruitment process to assist in screening, assessing, and selecting applicants for this position. This includes tools to evaluate candidate skills, experience, and qualifications against the role requirements.
AI tools are used to assist our hiring team, not to make final hiring decisions. All candidate applications and interview outcomes are reviewed and evaluated by people. If you have questions about how AI is used in our hiring process, please reach out.