Hybrid Seattle Data Platform Engineer — Healthcare AI

Xealth; Seattle (WA); Hybrid; USD 150,000 - 175,000; Full time

Benefits offered by this job

Hybrid Seattle office

Competitive compensation

Home office stipend

Comprehensive medical, dental, vision

401k match

Flexible time off

Job summary

Xealth is seeking a data engineer to design and operate scalable data pipelines in a healthcare context. You will work from our Seattle office in a hybrid role, collaborating with cross-functional teams to transform data into actionable insights for clinicians and patients.

The role emphasizes building robust data systems, implementing CI/CD practices, and leveraging cloud-native technologies to support real-time analytics and decision-making in a regulated environment.

Qualifications

• We’re seeking a data engineer with strong CS fundamentals and scale-focused thinking.
• 3+ years building production software with attention to quality and testing.
• Deep understanding of distributed computing, concurrency, and data pipelines.

Responsibilities

• Design scalable, maintainable systems for high-volume data processing.
• Ingest and process real-time and batch data from various sources.
• Scale Data Lake pipelines for performance and cost efficiency.
• Profile, debug, and optimize distributed workloads.
• Write unit and integration tests for data pipelines.
• Collaborate with product managers and EHR specialists to derive insights.
• Document architecture decisions and system references.
• Leverage AI-assisted tooling to accelerate delivery and quality.

Skills

System design

Data ingestion

SQL/NoSQL

AWS

Kubernetes

Testing

API design

Agile

Tools

Delta Lake

Parquet

Avro

Terraform

Airflow

RESTful APIs

Job description

Xealth is seeking a data engineer to design and operate scalable data pipelines in a healthcare context. You will work from our Seattle office in a hybrid role, collaborating with cross-functional teams to transform data into actionable insights for clinicians and patients.

The role emphasizes building robust data systems, implementing CI/CD practices, and leveraging cloud-native technologies to support real-time analytics and decision-making in a regulated environment.