About Amigo

Amigo partners with healthcare organizations to deploy robust AI infrastructure that directly serves patients and providers. Our agents handle clinical workflows and patient engagement across the entire journey: pre-visit intake, care navigation, post-visit care plans, patient monitoring, and more.

We own outcomes, not just delivery. For our customers, we're responsible for agent performance: clinical safety, continuous improvement, measurable patient outcomes. Agents operate autonomously within bounded clinical domains, with clear scope and handoff protocols. That scope expands as we validate performance across populations.

We're backed by Tier 1 investors like General Catalyst, GSV Ventures, SVA, and CohoVC. Our work is validated with leading academic medical institutions. Our agents have reached 3M+ patient encounters and are on track to 10x this year.

About this role

As a Staff Software Engineer (Data) at Amigo, you'll own the technical direction of our data platform—a strategic differentiator that powers agent improvement, clinical analytics, and research collaboration. You'll architect streaming and batch infrastructure on Databricks that processes agent conversations, clinical events, and patient outcomes at scale.

We own the entire data foundation: raw interaction data, agent reasoning traces, clinical outcomes, and high-fidelity synthetic data. You'll drive architecture decisions for population analysis, data mining pipelines, the Research Platform backend, and secure data sharing with academic partners.

What you'll do

• Own technical architecture for the data platform across Databricks, Delta Lake, and supporting infrastructure

• Drive engineering standards for pipeline reliability, data quality, and observability

• Architect streaming and CDC pipelines that power real-time analytics and agent feedback loops

• Design the data backend architecture for Research Platform, including natural language query capabilities

• Architect data mining systems for persona discovery, scenario extraction, and edge case detection

• Design anonymization and data sharing infrastructure for research partnerships with academic medical institutions

• Own multi-region data architecture and compliance requirements

• Make build vs. buy decisions for data tooling and evaluate technical tradeoffs

• Mentor engineers and establish patterns that raise the bar for the data team

• Collaborate with data scientists, agent engineers, and clinical operations to align data capabilities with business needs

What we're looking for

• 7+ years of production data engineering experience, with significant time at high-caliber engineering organizations

• Expert-level experience with Databricks, Spark, and Delta Lake at scale

• Strong Python and SQL skills with deep understanding of distributed data systems

• Proven track record designing data architectures that scale

• Deep experience with streaming systems, CDC patterns, and real-time data processing

• Strong understanding of data modeling, medallion architecture, and query optimization

• History of establishing engineering standards and mentoring engineers

• Extremely high standards for data quality, reliability, and operational excellence

• Both execution-oriented and defensive-minded: you ship infrastructure while anticipating failure modes

• Excellent communication across engineering, data science, and executive stakeholders

Nice to have

• Experience with healthcare data platforms or HIPAA compliance at scale

• Background architecting multi-tenant data systems with strict isolation requirements

• Experience building natural language query interfaces or LLM-powered data tools

• Track record with ML infrastructure (feature stores, training pipelines, model serving)

• Experience with Delta Sharing or cross-organization data collaboration

• Knowledge of vector search systems and embedding infrastructure at scale

Benefits

Health & Wellness

• Comprehensive health, dental, and vision insurance

• Daily catered lunch and dinner

• Mental health support and wellness coaching

• Flexible wellness stipend for fitness, therapy, or personal growth

Growth & Development

• Annual learning budget for courses, books, or conferences

• Conference attendance budget for professional development

• Annual team offsite

• Academic collaboration opportunities

• Unlimited PTO

Our Core Values

1. Patients Win, We Win

   If patients aren't getting better care, we haven't earned the right to scale. Every internal decision gets pressure-tested: does this make patients' lives better? If we can't draw the line, we question why we're doing it.

2. High Standards, High Care

   We hold a high bar for the team because patients are counting on us to get this right. But high standards only work with genuine investment in each other. You can take risks, admit mistakes, and challenge ideas—not despite our standards, but because of them.

3. Thoughtful Urgency

   We move fast by default, but speed without judgment is recklessness. The discipline is knowing which decisions are reversible vs. not. In healthcare AI, the companies that win will be fast everywhere they can be and careful everywhere they must be. We build the muscle to do both.

4. Intensely Measured

   We instrument patient outcomes, provider ROI, system performance, and clinical accuracy. But data without action is surveillance. Every metric should have an owner, a threshold, and a response plan. If we're measuring something but never acting on it, we stop measuring it.

Who Builds With Us

• Low ego: Politics and territory don't interest you. The best ideas win, regardless of who has them.

• Direct: You say the hard thing, challenge ideas openly, and commit fully once decided.

• High agency: You thrive on trust rather than instruction. When you see something is broken, you fix it. You don’t file tickets and wait for someone else.

• Bar of excellence: You hold yourself to a bar most people wouldn't, and you want teammates who do the same.

• Skeptical: You push back on rules that don’t make sense and question assumptions that haven’t earned their place.