Sr. Engineer, Data

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

7d•$101,600 - $175,200•Hybrid

About The Position

The Senior Data Engineer plays a critical role in implementing Ensemble’s data engineering strategy. This role focuses on building scalable, reusable data pipelines and platforms that enable analytics, operational reporting, and innovation across the organization. The engineer partners closely with peers and stakeholders, influences engineering standards through design and code reviews, and contributes directly to long-term platform modernization.

Requirements

6+ years of hands-on experience developing solutions using Microsoft SQL.
3+ years working with big data technologies such as Databricks, Apache Spark, Python, and Microsoft Azure (ADF, Dataflows, Azure Functions, Service Bus).
Strong understanding of engineering fundamentals including automated testing, code reviews, telemetry, iterative delivery, and DevOps.
Experience with polyglot storage architectures including relational, columnar, key-value, and graph systems.
Hands-on experience with Delta Lake tables and Parquet data stored in ADLS.
Experience building distributed, componentized applications using event-driven patterns.
Ability to communicate effectively with both technical and non-technical, globally distributed audiences.
Solid foundation in software architecture, design patterns, and engineering best practices.
Experience working with healthcare datasets; familiarity with HL7 or EDI is a plus.
High attention to detail and a strong sense of ownership.
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

Familiarity with HL7 or EDI.

Responsibilities

Develop, test, deploy, monitor, and continuously improve scalable data pipelines and API integrations.
Apply generative AI tools in day-to-day engineering work to accelerate development and improve quality.
Translate product and business concepts into incremental, high-quality technical deliverables.
Contribute through hands-on design sessions and thorough code reviews.
Implement data quality monitoring to ensure accurate, reliable production data.
Review and recommend architectural patterns aligned with enterprise platforms and best practices.
Consistently applies generative AI in day‑to‑day engineering work - using it to accelerate development, improve code quality, troubleshoot complex systems, and design scalable data solutions.