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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