Data Engineer

Ensemble Health Partners•Work at Home - Ohio - Other, OH

2d•$88,200 - $132,300•Remote

About The Position

The Data Engineer II supports Ensemble’s data engineering strategy by designing, building, and maintaining reliable data pipelines and integrations that enable analytics, operational reporting, and data-driven decision making. This role focuses on hands-on development within established platforms and patterns, working closely with senior engineers and stakeholders to deliver high-quality, scalable solutions. The engineer contributes to platform modernization efforts while continuing to deepen technical expertise and engineering best practices.

Requirements

3+ years of hands-on experience developing solutions using Microsoft SQL.
2+ years of experience with big data and cloud technologies such as Databricks, Apache Spark, Python, and Microsoft Azure (ADF, Dataflows, Azure Functions, Service Bus).
Working knowledge of engineering fundamentals including automated testing, code reviews, telemetry, and DevOps practices.
Experience working with EDI
Experience working with modern data storage formats such as Delta Lake and Parquet in ADLS.
Familiarity with distributed data processing and event-driven architectures.
Ability to collaborate effectively with technical and non-technical partners.
Strong attention to detail and accountability for owned deliverables.
Must be inquisitive and demonstrate openness to innovation including AI to explore better processes and ways to alleviate friction and improve patient and client experiences.

Nice To Haves

Experience working with healthcare data is preferred; familiarity with HL7 or EDI is a plus.

Responsibilities

Develop, test, deploy, and maintain data pipelines and API integrations using established patterns and standards.
Use generative AI tools to assist with development, troubleshooting, and improving code quality under guidance.
Translate well-defined product and business requirements into reliable technical solutions.
Participate in design discussions and contribute to code reviews to improve solution quality and consistency.
Implement and support data quality checks to ensure accurate and reliable production data.
Follow approved architectural patterns and contribute feedback for continuous improvement.
Monitor pipelines and resolve data issues in partnership with senior engineers.
Document solutions, processes, and operational considerations clearly for reuse and support.