Virta Health is on a mission to reverse metabolic disease in one billion people. Current treatment approaches aren’t working—over half of US adults have either type 2 diabetes or prediabetes, and obesity rates are at an all-time high. Virta is changing this by helping people reverse their metabolic condition through innovations in technology, personalized nutrition, and virtual care delivery reinvented from the ground up. We have raised over $350 million from top-tier investors, and partner with the largest health plans, employers, and government organizations to help their employees and members restore their health and take back their lives. Join us on our mission to reverse metabolic disease in one billion people.
This role is a blend of real-world impact, technical challenge, and modern tooling. You’ll help build and evolve Virta’s data engagement platform — the system that turns partner and internally derived data into eligibility decisions and campaign-ready member audiences.
You’ll anchor the engineering behind Member Marketing, which ensures the right members hear about Virta at the right time, and the Coverage Eligibility data it depends on, which determines whether someone can access life-changing care.
You’ll create a trusted, observable path from raw data to member outreach: making identity, deployment context, data freshness, and transformation behavior explainable; designing safe recovery and replay mechanisms; and building reusable data capabilities that replace one-off operational workflows.
You’ll modernize file- and API-based data exchange through explicit contracts, automated validation, observable workflows, and event-driven integrations where appropriate — reducing manual intervention without assuming every partner can adopt the same integration model.
We work in an AI-augmented way. You’ll bring sound judgment about where AI-assisted tooling improves velocity, data-quality investigation, and operations — and where deterministic controls are required for correctness and safety.
If you’re excited to own foundational data infrastructure, turn ambiguous growth requirements into durable technical contracts, and lead a squad solving hard data-correctness problems, this is a place where you can thrive.
ResponsibilitiesDesign and own the data contracts and processing semantics that make identity, deployment context, eligibility, audience attributes, deduplication, and field persistence explicit and testable across the data lifecycle.
Build end-to-end observability and reconciliation that explains how source records become downstream outcomes — including data freshness, expected filtering, rejected records, deduplication, safe replay, and repair of historical state.
Lead technical discovery and delivery of high-impact work across Member Marketing and Coverage Eligibility, collaborating closely with Product, data, and external partners.
Partner with Product and Growth stakeholders to translate evolving audience and workflow requirements into reusable platform capabilities, keeping business policy cleanly separated from technical implementation.
Design for correctness and auditability in regulated workflows, including data provenance, consent and suppression controls, rollback, replay, and safeguards against incorrect or cross-customer data use.
Modernize legacy, file-based processes toward well-contracted, event-driven integrations with strong observability, resilience, and security.
Own systems end-to-end — from architecture through operations — driving improvements in reliability, partner experience, and long-term maintainability.
Apply an experiment mindset (Build-Measure-Learn) to validate assumptions and focus on outcomes over output, using AI-assisted tooling where it improves speed and quality without compromising correctness.
Within your first 90 days at Virta, we expect you will do the following:
First 30 Days: Learn & Connect
Understand Virta’s data ecosystem — architecture, key data flows, systems for Coverage Eligibility and Member Marketing, and how partners interact with Virta’s platform.
Get familiar with existing processes and pain points around file-based exchanges, partner onboarding, data quality, and incident patterns.
Build relationships with key stakeholders: engineering teammates, product managers, the data team, and partner-facing roles.
Observe real-world partner data issues, reviewing tickets, Slack threads, and postmortems to identify recurring themes.
Days 31–60: Contribute & Experiment
Ship production code: contribute meaningfully to an in-flight initiative touching data flows or partner integrations.
Identify and propose a targeted improvement using the Build-Measure-Learn loop — e.g., automating a partner data-quality check or prototyping stage-level reconciliation.
Start building technical credibility across the team by writing clean, scalable code and actively participating in design reviews.
Days 61–90: Lead & Shape
Own a meaningful workstream — such as a small net-new service, a platform capability (e.g., data validation or status tracking), or a cleanup/migration effort.
Drive an architectural discussion that involves tradeoffs across scalability, observability, and correctness.
Show thought leadership by pushing forward better patterns (e.g., eventing over polling, stronger test coverage, clearer data contracts).
Build trust through delivery — others should be seeking your input and starting to rely on you as a technical thought partner.
Strong backend engineering skills, with a track record of building and operating scalable, reliable systems in production — ideally involving APIs, event-driven architectures, or data integrations.
Experience designing data-intensive systems where correctness depends on explicit schemas, identity or entity resolution, incremental processing, idempotency, lineage, reconciliation, and recovery from partial failure.
A proactive, ownership-first mindset — you don’t wait for direction; you surface problems, propose solutions, and drive them to completion.
High agency and autonomy — able to navigate ambiguity, set direction, and execute independently while keeping stakeholders aligned.
Demonstrated technical leadership — able to establish architecture and engineering standards across team boundaries, mentor senior engineers, and turn ambiguous business problems into a sequenced technical roadmap.
Experience with B2B or partner-facing data systems, such as file-based integrations, real-time data exchanges, or partner APIs.
Sound judgment about AI-assisted tooling — where it improves engineering velocity, data-quality investigation, documentation, and operations, and where deterministic controls are required for correctness and safety.
Strong collaboration and communication skills, especially in cross-functional environments involving Product, data, and external partners.
Typically 10 + years of software engineering experience, with demonstrated ability to operate at a senior level.
Experience in healthcare data domains, such as eligibility, claims, EHR, or member marketing — especially in regulated or partner-integrated environments.
Experience with customer-data, marketing-automation, or audience-activation platforms such as Braze, Segment, Salesforce Marketing Cloud, or comparable systems.
Prior work modernizing legacy data flows, including migrations from SFTP/file-based systems to API- or event-driven architectures.
Familiarity with data quality frameworks, lineage tracking, or automated anomaly detection in data pipelines.
Demonstrated use of AI/LLM-based tooling in engineering work — e.g., for test generation, documentation, data mapping, or observability.
Previous roles where you've had to influence without formal authority, or drive change across teams or systems you didn’t own directly.
We don’t expect candidates to know everything on day one, but here are some of the tools we currently use day-to-day:
Cloud & Infrastructure
Google Cloud Platform: GKE (Kubernetes), Workflows, Pub/Sub, BigQuery, CloudSQL, Memorystore, Gemini
Data & Modeling
SQL, BigQuery, dbt, data contracts, lineage, reconciliation, and data-quality testing
Dev & Collaboration
GitHub + GitHub Actions for CI/CD
Google Workspace for docs, specs, and async collaboration
ChatGPT + OpenAI platform to accelerate development, testing, documentation, and product use cases
Languages & Tooling
Golang (primary language for backend services)
Python (for data tooling, automation, AI workflows)
JavaScript/TypeScript (for internal tools and platform UIs)
Virta’s company values drive our culture, so you’ll do well if:
You put people first and take care of yourself, your peers, and our patients equally
You have a strong sense of ownership and take initiative while empowering others to do the same
You prioritize positive impact over busy work
You have no ego and understand that everyone has something to bring to the table regardless of experience
You appreciate transparency and promote trust and empowerment through open access of information
You are evidence-based and prioritize data and science over seniority or dogma
You take risks and rapidly iterate
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Virta has a location based compensation structure. Starting pay will be based on a number of factors and commensurate with qualifications & experience. For this role, the compensation range is $176,500 - $228,000. Information about Virta’s benefits is on our Careers page at: https://www.virtahealth.com/careers.
As part of your duties at Virta, you may come in contact with sensitive patient information that is governed by HIPAA. Throughout your career at Virta, you will be expected to follow Virta's security and privacy procedures to ensure our patients' information remains strictly confidential. Security and privacy training will be provided.
As a remote-first company, our team is spread across various locations with office hubs in Denver and San Francisco.
Clinical roles: We currently do not hire in the following states: AK, HI, RI
Corporate roles: We currently do not hire in the following states: AK, AR, DE, HI, ME, MS, NM, OK, SD, VT, WI.
Virta uses Ashby as its applicant tracking system, which incorporates AI-powered tools (provided by OpenAI, AWS, and Google Gemini) in certain aspects of the recruiting process, including application review, candidate screening, and interview note taking; your data is not used to train AI models, and all final hiring decisions are made by Virta Health personnel. For more information, see Ashby's AI Terms at https://www.ashbyhq.com/resources/terms-ai-features
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