Lead a high-performing data engineering team that fosters a culture of technical excellence and continuous learning. Oversee the end-to-end data engineering development, from ingestion to transformation and delivery in Databricks, ensuring reliability in alignment with business objectives. Lead the migration of data integrations, pipelines, architectures, and existing platforms to Databricks. Guide the adoption of best-in-class engineering practices, emphasizing robust testing, monitoring, security, and data quality enforcement. Set high standards for technical excellence, promoting clean architecture and code quality through mentorship and hands-on guidance. Collaborate with teams across various functions, including business management, business development, facilities, human resources, supply chain, and manufacturing. Must have, at minimum, a Bachelor's degree in Computer Science, Engineering, or a related field. Must have a minimum 12 years of experience in data engineering or a related technical field, including experience managing scalable data platforms. Must have a minimum of 2 years of experience in people management and/or leading technical teams. Experience building out data models and medallion architecture in Databricks or similar modern data platforms. Experience leading teams to design and implement data models, data warehouses, and overall data architecture, weighing trade-offs. Strong communication and stakeholder management skills, with a demonstrated ability to influence both technical and executive audiences. Master's degree or PhD in Engineering or related field. Experience integrating data from enterprise systems such as SAP, Workday, ServiceNow, and Salesforce. Deep domain expertise in one or more of the following areas:

Finance, Supply Chain, Human Resources, or Manufacturing