Director or VP of Data Engineering

AcuityMD is a software and data platform that accelerates access to medical technologies. As the Director or VP of Data Engineering, you will shape the systems that define how the company understands healthcare at scale and make critical technical and leadership decisions that impact medical innovation and patient care.

Responsibilities:

• Set and own the technical and organizational north star for data engineering at AcuityMD—defining how we build the world’s most accurate and actionable model of healthcare reality
• Lead the design, implementation, and evolution of our core data platform, powering every aspect of our product and agentic experiences—from analytics and workflows to AI-driven insights
• Translate company and product strategy into clear data investments, and clearly articulate the customer value of those investments to engineering, product, and leadership partners
• Go deep when it matters: drive architectural decisions, review critical designs, debug hard problems, and coach the team through complex technical tradeoffs
• Build data systems that are constantly improving, reliable, and trusted—supporting massive scale, complex healthcare data, and customer-facing use cases where correctness truly matters
• Partner closely with product, data science, and engineering leaders to ensure tight feedback loops between data generation, modeling, and real-world customer outcomes
• Lead, mentor, and grow a high-impact team of data engineers, data scientists, and domain experts, setting a high bar for methodological and technical rigor, ownership, and velocity
• Establish strong—but pragmatic—standards for data quality, testing, observability, lineage, and governance, without slowing the team down
• Shape the culture of the data organization: how we plan, how we ship, how we review work, and how we learn from mistakes
• Stay close to the evolving state of modern data engineering and analytics engineering, bringing in new ideas when they meaningfully raise our ceiling

Requirements:

• 10+ years of professional experience in data engineering, data platforms, or closely related software engineering roles, including significant technical leadership experience
• You're as comfortable discussing schemas, query performance, and orchestration as you are discussing product strategy, customer impact, and team design
• You are deeply hands-on and have designed, built, and operated production data systems that matter—pipelines, transformations, analytical models, and platforms that real users rely on
• You're fluent in Python and SQL, and have extensive experience working with modern cloud data warehouses, especially BigQuery
• You have strong product instincts and enjoy working directly with product managers and data scientists to shape what gets built and why
• You've led teams before and genuinely enjoy coaching, mentoring, and raising the bar for others
• You think in systems: architecture, incentives, workflows, and failure modes—not just individual pipelines or tools
• You communicate clearly and directly, can explain complex tradeoffs without hand-waving, and bring people along with you
• You thrive in fast-moving, ambiguous environments and are excited by building foundational systems from an early stage
• You care deeply about data quality, correctness, and trust—especially when data drives high-stakes decisions
• You love working on a low-ego, high-ownership team building products that materially change how an industry operates
• You have a BA/BS or MA/MS in Computer Science, Engineering, Mathematics, or a related field—or equivalent practical experience
• Deep experience with our stack: Python and SQL on GCP, with heavy BigQuery usage, plus tools like dbt, Dagster, Docker, and Kubernetes
• Experience building data platforms that directly power customer-facing products and AI or agentic workflows
• A background that spans data engineering and data science or ML-adjacent systems
• Strong opinions about modern data architectures, analytics engineering, and the evolving role of data teams, backed by deep experience, that you're constantly seeking to invalidate and refine
• You've earned real scars from pipelines breaking, data being wrong, or systems buckling under scale—and you've used those lessons to build calmer, more resilient platforms