Software Engineer, Data Platform

At Xealth, we're revolutionizing healthcare by leveraging data and automation to empower care providers (building on EHRs such as Epic and Cerner) to seamlessly prescribe, deliver, and monitor digital health for patients. We are a detail-oriented team, committed to maintaining the highest standards while moving with agility and impact.

We are a highly skilled, collaborative, and passionate group, applying our expertise to improve health outcomes for millions. We believe in shared ownership and are looking for a team player who is a self-starter and self-driven to pioneer the next generation of intelligent, automated cloud infrastructure.

This role offers a unique opportunity to join a data engineering team to advance our capabilities with data processing pipelines and our analytics product offering.
* This position is a hybrid role, working from our Seattle office.

Please note - you MUST be a U.S. Citizen or Permanent Resident to be hired*

As a software engineer on our data platform team, you will design and build scalable systems, write maintainable and well-tested code, and own end-to-end engineering quality across the services that drive Xealth's Analytics and Reporting Capabilities. You'll apply solid computer science fundamentals to solve complex problems in distributed systems, data modeling, and data pipelines.

• System & Architecture Design:
  Design scalable, maintainable systems for high-volume data processing. Build robust abstractions and well-structured APIs that other engineers can build on with confidence.
• Data Ingestion:
  Ability to consume and process high-volume bounded and unbounded data, manage real-time data streams, and gather data from API calls and webhooks.
• Scalability & Maintenance:
  Maintain and scale large Data Lake Pipelines, ensuring high performance and cost-efficiency.
• Performance & Optimization:
  Profile, debug, and optimize systems for performance and cost. Identify and remove bottlenecks across distributed workloads.
• Unit testing & Quality Assurance:
  Write comprehensive unit and integration tests for data pipelines to ensure code quality and production reliability.
• Cross-Functional Collaboration:
  
  Partner with product managers and EHR specialists to translate clinical user behaviors into rich, analytical datasets, unlocking critical insights that drive evidence-based improvements in healthcare processes.
• Documentation:
  Produce clear technical documentation — design docs, architecture decisions, and system references that keep knowledge shared and durable.
• AI-Assisted Development:
  Use AI-assisted coding tools (e.g., Claude, Git Hub Copilot) to accelerate delivery and raise quality, and help the team adopt effective agentic workflows.

Must Have:

• Software Engineering Fundamentals:
  : 3+ years building production software, with strong command of SOLID principles, design patterns, and clean architecture. You care about code quality and know how to make it stick through review and testing.
• CS Fundamentals:
  Deep understanding of algorithms and data structures, with a specific focus on distributed computing principles (concurrency, partitioning, shuffling) necessary for processing large-scale datasets.
• Testing Practices:
  Fluency across unit, integration, and end-to-end testing. You design for testability and treat automated tests as core to shipping.
• Languages:
  
  Strong proficiency in Python and SQL; ability to pick up new languages and frameworks quickly. Familiarity with JavaScript is a plus.
• Modern SQL and Non-SQL Database Design:
  Deep practical knowledge of open table formats, such as Delta Lake. Proficiency with common big data file formats, including Apache Parquet and Apache Avro.
• Infrastructure as Code (IaC):
  Experience implementing IaC principles and tools for the automated deployment and management of data pipelines.
• API & Integration:
  Hands-on experience designing robust data ingestion frameworks via RESTful APIs and building event-driven architectures for real-time data flow.
• Cloud & Distributed Systems:
  Experience designing and scaling cloud-native data platforms and orchestrating data workloads using AWS and Kubernetes.
• Collaboration and Agility:
  Comfortable working in an agile startup environment, balancing quality with rapid iteration.
• Agentic Systems & LLM Integration:
  Experience with agentic systems, Claude/LLM integration, or AI-assisted development workflows.
• Observability:
  Experience implementing…