Senior Data Architect, Data & Analytics

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

athenahealth is building an AI-first future for healthcare and our data platform is foundational to making that future real. We are seeking a Senior Architect to lead the architecture and drive that foundation for our Data & Analytics Platform, a critical capability that powers analytics, intelligence, and AI across the athenahealth ecosystem.

This role owns the architect strategy and outcomes for how data is ingested, modeled, governed, and activated - supporting everything from real-time analytics and observability to embedded intelligence, customer insights, and commercial data products.

Reporting to the VP of Engineering in Data & Ecosystem Platform, this influential leader will work with key technical leaders across the org to drive our data and analytics architecture well in the future and use our data as an advantage balancing cost, latency, security, compliance, and operational simplicity. They will demonstrate exceptional design abilities and engineering excellence with data at scale.

Key Responsibilities

• Own end-to-end architecture for athenahealth’s Data & Analytics Platform (ingestion - storage - compute - semantic/servicing layers). Produce accurate, unambiguous technical design specifications that will be accepted by the broader engineering leadership community.

• Identify and influence solutions to complex technical issues. Build prototype and PoC’s to promote new technologies and processes.

• Act as a trusted peer to AI/ML leaders, shaping how data platforms accelerate copilots, agents, and embedded intelligence.

• Lead by example, delivering high‑quality code for critical sections of the product and/or platform; champion quality standards, performance and scalability.

• Provide cross‑functional coordination and communication, and mentorship of less senior team members.

• Focus on data governance, security, and compliance.

Leadership & Collaboration

• Partner closely with Product leadership to align architectural decisions and execution realities with product strategy.

• Act as a trusted peer to AI/ML leaders, shaping how data platforms accelerate copilots, agents, and embedded intelligence.

• Serve as a visible leader across athenahealth, translating complex platform concepts into clear, outcome‑oriented narratives.

• Experience leading working teams across geographies, with the ability to engage in difficult conversations to achieve goals while maintaining positive relationships.

• Must be laser‑focused on objectives and capable of influencing both technical and non‑technical teams.

Qualification and Experience

• years’ experience as an architect/leader including senior/principal-level ownership of large‑scale data systems.

• Deep knowledge of data lake/lakehouse architecture: ingestion, storage formats, compute engines, metadata/catalog, governance as well as database experience.

• Proven experience integrating heterogeneous sources across on‑prem + cloud, including migration/hybrid patterns.

• Strong fundamentals in distributed systems, data modeling, and performance optimization (partitioning, indexing, clustering, caching).

• Experience with AI – ML or LLM/Agentic frameworks.

• Expertise in modern programming object‑oriented languages such as C#, C++, Perl, Java, Python, and JavaScript/React.

• Understanding of scalable, cloud architecture and experience in applying them to real‑world problems.

• Experience in healthcare data ecosystems or other regulated environments and/or data commercialization with strong governance controls a plus.

• Knowledge of health interoperability standards (FHIR, HL7, ANSI) and different healthcare systems (EHR, EMR, CDS) a plus.

What success looks like (examples)

• Reduced total cost of ownership for analytics workloads (measurable storage/compute savings, improved utilization).

• Faster time‑to‑data for priority use cases (improved ingestion onboarding, lower pipeline latency, fewer manual steps).

• Stronger trust in data (quality SLAs, lineage, clear dataset ownership, consistent definitions).

• Improved security posture and audit readiness…