Software Engineer - Data Platform

R37 Lab, R1 RCM•Austin, TX

14h•Hybrid

About The Position

The role will own the data foundations of the Phare stack, including the backend schemas and APIs that power both our AI engine and user-facing applications. The engineer will work on reliable systems for ingesting, transforming, and serving large-scale healthcare data, ensuring high performance, observability, and security/compliance. The company is hiring across several seniority levels ranging from Mid-level up to Staff.

Requirements

Minimum of 5 years of software engineering experience.
Minimum of 2 years working with high-throughput data pipelines.
Strong in Scala and SQL, with experience building high-throughput production-grade data systems.
Experienced in architecting and building microservices and ETL pipelines in Python, Go, or Java.
Strong 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.
Experienced 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.
Experience working on the data backend behind a production-grade ML system and/or a user-facing SaaS application.

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.
Design and build microservices and API-driven integrations with external systems and trading partners.
Implement observability and reliability practices, using logs and metrics to proactively identify latency, data quality, and production issues before they impact users.
Own and operate production systems, including participating in a structured on-call rotation.