Lead Data Engineer

OneOncology is positioning community oncologists to drive the future of cancer care through a patient-centric, physician-driven, and technology-powered model. The Lead Data Engineer will lead teams responsible for data operations and analytics engineering, ensuring the reliability and performance of the data platform.

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

Requirements:

• 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
• Experience with large-scale distributed data processing