Remote Data Engineering Jobs in Tennessee (NOW HIRING)

Why join us? This is an exciting time to join OneOncology. Our values-driven culture reflects our startup enthusiasm supported by industry leaders in oncology, technology, and finance. We are looking for talented and highly-motivated individuals who demonstrate a natural desire to improve and build new processes that support the meaningful work of community oncologists and the patients they serve.

Job Description:

Role Summary:

OneOncology is seeking a Lead Data Engineer for a full-time role based in Austin, TX, Nashville, TN, or remote. Our team members are all individually motivated, yet collaborative. We maintain a high degree of accountability, are extremely productive and believe in enjoying our time at work.

The Lead Data Engineer will lead two closely related teams: a Data Operations team responsible for the reliability and performance of our Databricks-based data platform, and an Analytics Engineering team responsible for transforming and modeling data for business consumption. In this role, you will set technical direction across both teams, drive operational excellence, and ensure our data platform and data models are reliable, performant, and trusted by the business. This is a hands-on technical leadership role that requires interfacing directly with stakeholders, IT, customers, and other Engineering teams. You should have good communication skills, be organized, and be highly productive.

Responsibilities:

• Lead, mentor, and develop engineers across both the Data Operations and Analytics Engineering teams

• Contribute to a culture of clear goals, open feedback, and continuous growth across the team.

• Promote collaboration and accountability while driving high standards for quality, efficiency, and continuous improvement.

• Lead incident management and resolution for pipeline failures, cluster issues, and data quality problems, driving thorough root cause analysis and preventative improvements

• Define and enforce operational standards, runbooks, and on-call practices for the team

• Manage and maintain Databricks Workflows and job orchestration, ensuring SLAs are consistently met

• Oversee the design, development, and maintenance of data models and transformations that serve business intelligence and analytics use cases

• Define and enforce analytics engineering best practices including modular transformation patterns, data testing, and code review standards

• Partner with data analysts and business stakeholders to understand modeling requirements and ensure data is accurate, accessible, and well-understood

• Additional responsibilities as assigned to help drive our mission of improving the lives of everyone living with cancer.

Required or Preferred Qualifications:

• 8+ years of hands-on experience with SQL development

• 8+ years of experience working with relational and non-relational databases, with a strong foundation in data modeling, schema design, and query optimization.

• 5+ years of professional experience developing scalable solutions using Python or a similar OO language

• Proficient in Databricks, Spark, and Delta tables; experience with large-scale distributed data processing preferred

• Hands-on experience operating and monitoring data pipelines at scale in a production environment

• Solid understanding of the Lakehouse and Medallion architectures

• Experience with Azure data services (ADLS Gen2, Azure Data Factory, Event Hubs, or equivalent)

• Familiarity with Unity Catalog for data governance, access control, and data lineage

• Proven experience with designing Data Integration/ETL pipelines, using such tools as Azure Data Factory or equivalent.

• Excellent communication skills with the ability to convey technical concepts and operational status to both technical and non-technical stakeholders

Essential Competencies:

• Attendance is an essential job function.

This job description does not contain a full listing of activities, duties or responsibilities required of this role. Duties, responsibilities and activities may change, or new ones may be assigned at any time with or without notice.