Data Engineer

Pyx Health is looking for a motivated and technically solid Healthcare Data Engineer to join our growing Data & Analytics team. In this role, you will contribute to building and maintaining our data infrastructure on Azure, work alongside senior engineers to develop reliable data pipelines, and collaborate with analytics teams to engineer data foundations that support our healthcare solutions. You will work with Databricks, Airflow, Python, and SQL to implement and improve data workflows in a collaborative, high-growth environment. Only candidates residing in the USA may apply. KEY RESPONSIBILITIES Pipeline Development & Maintenance Build and maintain data pipelines for ingesting, transforming, and cleaning healthcare data in the Azure cloud using Databricks, PySpark, and Delta Lake Implement pipeline logic from defined specifications, with guidance from senior engineers on architectural decisions Build reusable testing frameworks to ensure pipeline reliability Monitor pipelines for failures and performance issues, escalating complex problems appropriately Orchestration & Automation Develop and maintain Airflow DAGs with appropriate error handling and retry logic Support deployment and configuration of pipelines via Astronomer on Azure using the Astro CLI Contribute to improving pipeline reliability and reducing manual intervention Data Modeling & Storage Implement data models for efficient storage and retrieval using Delta Lake, including merge/upsert patterns Design and optimize Delta Lake tables for analytic workloads Leverage Unity Catalog for data governance, organization, and security Apply Change Data Capture (CDC) patterns under the direction of senior team members Write and optimize T-SQL and Databricks SQL scripts, stored procedures, and ETL support Azure Infrastructure Work within the Azure ecosystem including ADLS Gen2, Key Vaults, Logic Apps, and Azure DevOps Follow established security and scalability standards when building data infrastructure Support infrastructure tasks with guidance from senior engineers on architecture Data Quality & Monitoring Ensure scripts and datasets are well-documented to support enterprise data governance Design, implement and continuously improve automated data quality monitoring, reconciliation, and alerting to ensure reliable downstream reporting Troubleshoot and resolve pipeline failures, documenting root causes and resolutions Collaboration & Documentation Work closely with data scientists, analysts, and business stakeholders to understand reporting requirements, troubleshoot issues, and improve data assets Participate in code reviews, incorporating feedback to improve code quality Document pipelines, processes, and implementation decisions clearly and consistently Stay current with data engineering technologies, particularly in the healthcare space