# Senior Software Engineer II

**Company:** [Virta Health](https://hotfix.jobs/companies/virta-health)
**Location:** Remote
**Role:** Data Engineering
**Salary:** $177k – $228k/yr
**Experience:** 10+ years
**Skills:** backend engineering, Data Pipelines, Event-Driven Architecture, APIs, data contracts, Observability, reconciliation, idempotency, lineage, entity resolution, GCP, GKE, AI Tools
**Posted:** 2026-07-31

> Build and own Virta Health's data engagement platform that powers Member Marketing and Coverage Eligibility decisions. Design observable, correct data contracts and modernize legacy integrations into event-driven systems while leading technical delivery in a regulated healthcare environment.

## Job Description

## Responsibilities
- Design 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.

## 90 Day Plan
**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.

## Must-Haves
- 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.

## Nice-to-Haves
- 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.

## Our Work Stack
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)

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