Principal Software Engineer, Electronic Dental Record - athenaCollector

Join us as we work to create a thriving ecosystem that delivers accessible, high-quality, and sustainable healthcare for all.

Join athenahealth as a Principal Software Engineer on our Electronic Dental Records (EDR) platform, where you'll serve as the top technologist driving the design and evolution of enhanced dental workflows. This is an execution cum leadership role. Along with the Architect, you'll set the architectural direction, define the engineering approach, and be accountable to drive the development teams on how the EDR platform is built, modernized, and scaled.

We're looking for an engineer with the depth to navigate complex legacy systems, the vision to define what comes next, and the leadership to bring teams along with them.

The EDR platform powers end-to-end dental practice workflows from clinical documentation and treatment planning to billing and claims. We are actively modernizing the platform, including a significant migration from .NET Azure to Java AWS, while simultaneously building enhanced workflows that raise the bar for dental providers on athenahealth. As Principal Engineer, you will be the technical anchor for this transformation, directing design decisions, establishing engineering standards, and ensuring the platform is built for long-term scalability, reliability, and maintainability.

• Own system-level architecture: Lead end-to-end architectural design across multiple product domains - claims processing, billing workflows, RCM - balancing long-term scalability, operational reliability, and delivery velocity. Your decisions set the standard other engineers build against.

• Define the AI-augmented engineering model: Go beyond personal use of AI tooling - establish team-wide patterns, guardrails, and best practices for integrating AI coding assistants, agentic tools, and LLM-assisted workflows into the full SDLC. Define what "responsible AI use" looks like in a healthcare-regulated environment.

• Drive spec-first culture at scale: Champion upstream investment in requirements clarity, acceptance criteria, and design reviews. Push product, design, and engineering to operate with enough precision that AI tooling and human review both run faster and with less ambiguity.

• Set the bar for AI output quality: Establish team standards for evaluating, debugging, and refining AI-generated code, tests, and designs. Ensure AI output is treated as a prototype - not a deliverable - and that correctness, security, and maintainability are never delegated to the model.

• Lead cross-team technical alignment: Serve as the primary technical voice across engineering teams across orgs. Drive resolution on architectural trade-offs, cross-team dependencies, and system-wide design decisions that no individual team can resolve alone.

• Prototype and product ionize AI-powered capabilities: Lead exploratory work on LLM-assisted features for internal developer productivity and client-facing product capabilities - defining safe, scalable, and regulation-aware patterns for AI application in healthcare.

• Evolve engineering practices: Own the evolution of code review norms, testing strategy, design review processes, and delivery practices across the organization. Identify systemic gaps and drive durable improvements.

• Grow the engineering organization: Mentor Senior and Staff engineers on architectural thinking, responsible AI use, and domain depth. Elevate the technical ceiling of the teams around you - not just through direct work but through the standards you set and the culture you model.

• Provide technical leadership on infrastructure decisions across AWS, Kubernetes, RDS including establishing AI-assisted infrastructure-as-code practices.

• Guide the adoption of emerging technologies where they materially improve platform capabilities. Contribute to the development of best practices and standards for software development.

• Define and oversee testing strategy at the system level - including AI-assisted test generation, integration coverage, and performance validation.

• Lead root-cause analysis and post-incident reviews for critical production issues; establish patterns that reduce repeat failures.

• Evaluate and recommend emerging AI-assisted development tools and practices - with a clear lens on what's production-ready vs. experimental.

• Represent engineering perspective in cross-functional planning, capacity discussions, and roadmap prioritization.

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.
• 12+ years of engineering experience in a Product Engineering organization, with demonstrated progression into technical leadership.
• Deep backend expertise in Java and Spring Boot; a track record of designing and shipping production-grade distributed systems.
• Proven experience…