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're fresh off our Series A backed by Tier 1 investors like Madrona, General Catalyst, and Optum Ventures. 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 roleAs a Staff Data Engineer at Amigo, you'll build the data layer everything else runs on. Healthcare organizations already keep their data in EHRs, schedulers, and warehouses. Your job is to pull that data in, keep it current, and turn scattered records into one accurate picture of each patient and provider. If the data is wrong or stale, everything above it breaks, so correctness is the work, not a nice-to-have. You'll own it end to end: ingestion, transformation, modeling, and the query tools teams use.
Building integrations that ingest and sync customer systems (EHRs, schedulers, warehouses, APIs)
Designing transformations that turn messy source data into one normalized model
Building and optimizing data pipelines that keep one clean profile per patient
Powering natural language query interfaces over healthcare data
Owning data modeling, query performance, and data freshness at scale
Built and operated production data platforms that ingest, process, and serve millions of events with high reliability
Designed scalable streaming and batch pipelines, data models, and ETL/ELT workflows for production systems
Possess deep expertise in SQL, distributed query optimization, and large-scale data processing
Have hands-on experience with modern data platforms such as Databricks, Snowflake, Delta Lake, Apache Iceberg, Spark, Kafka, or similar technologies
Designed event-driven architectures, change data capture (CDC), online serving systems, or reverse ETL pipelines
Built connector frameworks or ingestion platforms that integrate enterprise applications and third-party data sources
Balance performance, scalability, cost, and operational simplicity when designing distributed systems
Own production systems end-to-end, including architecture, implementation, monitoring, reliability, and incident response
Value simple, maintainable solutions, communicate directly, and maintain a high engineering bar with a low-ego, collaborative approach
You can work on site in New York City or San Francisco
Experience building data platforms in regulated or high-reliability industries such as healthcare, financial or services
Familiarity with healthcare data standards such as FHIR, HL7, or other clinical interoperability frameworks
Experience building data platforms that power production AI, machine learning, or agentic applications
Familiarity with modern lakehouse technologies such as Delta Lake, Apache Iceberg, or Unity Catalog
Experience with streaming platforms, change data capture (CDC), or event-driven architectures
Experience operating large-scale data platforms with a focus on reliability, observability, and cost efficiency
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
Annual learning budget for courses, books, or conferences
Conference attendance budget for professional development
Annual team offsite
Academic collaboration opportunities
Unlimited PTO
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.
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.
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.
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.
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.
Skills Required
- Built and operated production data platforms that ingest, process, and serve millions of events with high reliability
- Designed scalable streaming and batch pipelines, data models, and ETL/ELT workflows for production systems
- Deep expertise in SQL, distributed query optimization, and large-scale data processing
- Hands-on experience with modern data platforms such as Databricks, Snowflake, Delta Lake, Apache Iceberg, Spark, or Kafka
- Designed event-driven architectures, change data capture (CDC), online serving systems, or reverse ETL pipelines
- Built connector frameworks or ingestion platforms that integrate enterprise applications and third-party data sources
- Owned production systems end-to-end, including architecture, implementation, monitoring, reliability, and incident response
- Ability to balance performance, scalability, cost, and operational simplicity when designing distributed systems
- Experience powering natural-language query interfaces or query tooling over large datasets
What We Do
Amigo AI builds trust and safety infrastructure for clinical agents—ensuring AI systems in healthcare provide quantified confidence when mistakes aren't an option. Our platform combines advanced simulation, verification, and recursive optimization to enable healthcare organizations to deploy AI with statistical guarantees about its behavior. We solve the fundamental challenge of reliable AI in critical domains through deterministic verification for clinical protocols and continuous drift detection for real-world performance. Our systems provide complete transparency—every AI decision is traceable and auditable, with quantified confidence intervals rather than black box predictions. Founded by technologists from Google, Meta AI, Databricks, Coda, and Plaid, we've built systems that let organizations make informed risk decisions about AI deployment in healthcare. Our interdisciplinary approach draws from computer science, economics, physics, and mathematics to tackle human-centric optimization problems where people and populations are at the center of every solution. We're actively working with healthcare organizations across digital health, cancer care, cardiac care, and personalized medicine to deploy AI systems that continuously learn and adapt from real-world feedback while maintaining verified safety boundaries. Our technology amplifies human expertise rather than replacing it, empowering domain experts to achieve outcomes neither could accomplish alone.
Why Work With Us
We build AI healthcare systems where 99% isn't good enough. Rapid growth—promotions in 3 months. Freedom to work your way: art museums or late nights. Tackle recursive optimization problems that ship to production. Your work directly impacts critical healthcare decisions. Diverse team from Google, Meta AI, Databricks solving problems that matter.
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