Staff Software Engineer - Data [NYC or SF]

Amigo•San Francisco, CA

3d•$260,000 - $300,000•Hybrid

About The Position

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. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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