Staff Software Engineer - Data [NYC or SF]

Amigo AI; New York (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Comprehensive health, dental, and VISI

Daily catered lunch and dinner

Mental health support

Wellness stipend

Learning budget

Unlimited PTO

Job summary

Amigo is hiring a Staff Data Engineer to own the data layer powering patient-centric AI. You will ingest, normalize, and maintain data from EHRs, schedulers, and warehouses, ensuring correctness and freshness. The role emphasizes end-to-end ownership of ingestion, transformation, modeling, and query tooling.

You will design scalable pipelines, optimize performance, and support natural language interfaces for healthcare data, working on site in NYC or SF with a small, craft-focused team.

Qualifications

• Built and operated production data platforms ingesting, processing, and serving millions of events.
• Designed scalable streaming and batch pipelines, data models, and ETL/ELT workflows.
• Deep expertise in SQL, distributed query optimization, and large-scale data processing.
• Hands-on with Databricks, Snowflake, Delta Lake, Iceberg, Spark, and Kafka.
• Experience with event-driven architectures, CDC, and reverse ETL pipelines.
• Developed connector frameworks or ingestion platforms integrating enterprise data sources.

Responsibilities

• Ingest and sync data from EHRs, schedulers, warehouses, and APIs.
• Transform data into a normalized patient/provider model.
• Build and optimize data pipelines for a single clean patient profile.
• Power natural language query interfaces over healthcare data.
• Own data modeling, query performance, and data freshness at scale.

Skills

SQL

Distributed query

Large-scale data

Databricks

Snowflake

Delta Lake

Apache Iceberg

Spark

Kafka

CDC

ETL/ELT

Data modeling

Data pipelines

Tools

Databricks

Snowflake

Delta Lake

Apache Iceberg

Spark

Kafka

Job description

Staff Software Engineer - Data [NYC or SF]

Engineering New York City On Site Full Time

US Visa and Green Card sponsorship available

About Amigo

At Amigo, we're building the clinical AI infrastructure that health systems, life sciences companies, payors, and government agencies run their patient-facing care on. Our agents work across the whole patient journey: pre-visit intake, care navigation, post-visit care plans, and ongoing monitoring. Our mission is to make excellent care accessible to every patient, not rationed by the limits of clinician time.

This hasn’t been solved because the underlying record was never built for it. EHRs were designed for billing, not for systems that learn. So instead of layering another point solution on top, we’re rebuilding the medical record as a timeline of clinical events and running agents on top of it that improve with every patient interaction. Unlike single-purpose chatbots or scribes, Amigo is the platform our customers use to build their own agents. That means our work has to hold up across specialties, workflows, and regulatory environments and not just one narrow use case.

Our agents have supported more than 4 million patient encounters and are on track to grow tenfold this year. Our work is validated through partnerships with leading academic medical institutions. We’re fresh off our Series A, backed by Tier 1 VCs such as Madrona, General Catalyst, and Optum Ventures.

We’re a small team of around 35 people, working in person in New York City and San Francisco. We care deeply about craft, clinical rigor, and the speed at which good ideas reach patients. If you want your work to be measured in outcomes rather than releases, we’d like to hear from you.

About this role

As 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.

What you'll do

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

What we're looking for

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

Nice to have

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

Benefits (available to Full-Time Employees)

• 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
• Annual team offsite
• Academic collaboration opportunities
• Unlimited PTO

Our Core Values

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.

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.

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.