Software Engineer - Data Platform

R37 Lab, R1 RCM•Austin, TX

19h•$140,000 - $300,000•Hybrid

About The Position

You’ll own the data foundations of the Phare stack, including the backend schemas, and APIs that power both our AI engine and user-facing applications. You will work on reliable systems for ingesting, transforming, and serving large-scale healthcare data, ensuring high performance, observability, and security/compliance. We are hiring across several seniority levels ranging from Mid-level up to Staff. At a minimum, we would expect 5 years of software engineering experience with 2 years working with high-throughput data pipelines. We prefer this role to sit at our Austin, TX hub.

Requirements

Minimum 5 years of software engineering experience.
Minimum 2 years working with high-throughput data pipelines.
Experience on the data backend behind a production-grade ML system and/or a user-facing SaaS application.
Strong in Scala and SQL.
Experience building high-throughput production-grade data systems.
Experience architecting and building microservices and ETL pipelines in Python, Go, or Java.
Experience with Databricks, Spark, Airflow, or Kafka for orchestrating and streaming data flows.
Comfortable managing dependencies, scheduling, and state in complex workflows.
Comfortable designing and building microservices and API-driven integrations with external systems and trading partners.
Experience building and managing infrastructure with Terraform, Docker, and Kubernetes.
Adept at implementing observability and reliability practices, using logs and metrics to proactively identify latency, data quality, and production issues before they impact users.
Comfortable owning and operating production systems, including participating in a structured on-call rotation.

Responsibilities

Own the data foundations of the Phare stack, including the backend schemas, and APIs that power both our AI engine and user-facing applications.
Work on reliable systems for ingesting, transforming, and serving large-scale healthcare data, ensuring high performance, observability, and security/compliance.
Architecting and building microservices and ETL pipelines in Python, Go, or Java.
Orchestrating and streaming data flows using Databricks, Spark, Airflow, or Kafka.
Managing dependencies, scheduling, and state in complex workflows.
Designing and building microservices and API-driven integrations with external systems and trading partners.
Building and managing infrastructure with Terraform, Docker, and Kubernetes.
Implementing observability and reliability practices, using logs and metrics to proactively identify latency, data quality, and production issues before they impact users.
Owning and operating production systems, including participating in a structured on-call rotation.