Lead Data Engineer

Accountabilities: Lead, mentor, and develop the Data Engineering team, setting clear expectations for performance, quality, accountability, and ownership. Manage team priorities, workload, and delivery plans in alignment with organizational objectives and the broader data roadmap. Provide technical direction and oversight across architecture, system design, development, testing, and production readiness. Serve as an escalation point for complex technical issues, data-quality challenges, and delivery risks. Design, build, maintain, and optimize scalable data pipelines, integrations, warehouses, and supporting infrastructure. Establish and maintain standards for data architecture, modeling, reliability, scalability, performance, maintainability, and data quality. Remain hands-on by contributing to development and resolving complex or business-critical technical problems. Improve automation, monitoring, tooling, and engineering practices to strengthen the reliability and efficiency of the data environment. Define technical direction and priorities for Data Engineering in partnership with business and technical leadership. Partner with Finance, Data & Analytics, and other stakeholders to understand business requirements and translate them into effective data solutions. Evaluate technologies, processes, and architectural approaches that improve the organization's ability to use, manage, and scale data. Communicate technical strategy, infrastructure needs, risks, and recommendations clearly to both technical and non-technical stakeholders. Track and improve key outcomes including pipeline reliability, data quality, delivery timeliness, roadmap progress, team development, and stakeholder satisfaction. Contribute to special projects and additional responsibilities as business needs evolve. Requirements 7+ years of professional data engineering experience, including at least 2 years leading, supervising, mentoring, or providing technical direction to other engineers. Bachelor’s degree in Computer Science, Data Engineering, a related technical discipline, equivalent technical training, or equivalent professional experience. Strong hands-on experience designing, building, and maintaining data pipelines, ETL/ELT processes, data warehouses, integrations, and supporting infrastructure. Advanced SQL capabilities and strong programming experience with Python, Java, Scala, or a comparable programming language. Deep understanding of data architecture, data modeling, scalability, performance optimization, reliability, and data-quality practices. Experience with modern cloud data platforms, orchestration frameworks, and data-processing technologies. Demonstrated ability to balance hands-on technical contribution with effective team leadership and development. Strong business acumen and the ability to translate business requirements into practical, scalable technical solutions. Excellent communication and stakeholder-management skills, with the ability to work effectively across technical and non-technical teams. Experience with AWS, Azure, or Google Cloud Platform is preferred. Familiarity with orchestration and transformation tools such as Airflow, dbt, or comparable technologies is preferred. Experience with distributed or streaming data technologies such as Kafka or Spark is a plus. Experience with infrastructure-as-code and CI/CD practices for data environments is advantageous. Experience establishing, scaling, or maturing a Data Engineering function is preferred. Strong ownership mindset, collaborative approach, and commitment to technical quality and continuous improvement. Benefits Remote work opportunity from within the United States. Leadership role with significant influence over data engineering strategy and technical direction. Opportunity to remain hands-on while leading and developing a Data Engineering team. Exposure to cross-functional initiatives spanning Finance, Data & Analytics, and other business functions. Opportunity to work with modern cloud data platforms, orchestration technologies, and scalable data infrastructure. Collaborative environment focused on ownership, performance, continuous improvement, and professional development. Flexible work environment supporting work from home. Reasonable workplace accommodations available for qualified individuals with disabilities.