Company:Driven Brands We invite you to join us at Driven Brands! Headquartered in Charlotte, NC, Driven Brands (NASDAQ: DRVN) is the largest automotive services company in North America, providing a range of consumer and commercial automotive needs, including paint, collision, glass, vehicle repair, oil change, maintenance and car wash. With over 4,500 centers in 15 countries, Driven Brands is the parent company of some of North America’s leading automotive service brands including Take 5 Oil Change, Take 5 Car Wash, Driven Glass, Meineke, Maaco, CARSTAR, and more. Our network services over 50 million vehicles annually and generates more than $5 billion in system-wide sales each year. Our culture inspires high performance and innovation, enabling our employees to go further, faster in their careers. With amazing people and great brands, we confidently look forward to exciting growth ahead, and believe in following the values that support this vision. JOB DESCRIPTION: We are seeking a dynamic and experienced Data and Analytics Engineering Manager to lead our data engineering team. This is a hands on role. In this role, you will be responsible for overseeing the design, development, and maintenance of our data infrastructure and analytics solutions. Your leadership will ensure the delivery of high-quality, scalable data systems that support the organization's strategic goals. You will collaborate with cross-functional teams to drive data initiatives, optimize data workflows, and ensure the reliability and efficiency of our data processes. Key Responsibilities: Team Leadership: Manage, mentor, and develop a team of data engineers, fostering a collaborative and high-performance work environment. Project Management: Oversee the planning, execution, and delivery of data engineering projects, ensuring they are completed on time and within budget. Data Architecture: Design and implement scalable, reliable, and secure data architectures that meet business needs and industry standards. Pipeline Development: Lead the development and optimization of data pipelines and ETL processes to ensure efficient data ingestion, transformation, and storage. Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and provide technical guidance and solutions. Data Governance: Implement and enforce data governance policies and best practices to ensure data quality, security, and compliance. Performance Optimization: Continuously monitor and optimize the performance of data systems, ensuring they meet the needs of the organization. Innovation: Stay up-to-date with emerging technologies and trends in data engineering and analytics, and evaluate their potential application within the organization. Reporting: Develop and maintain metrics and dashboards to monitor the performance and health of data systems, and provide regular reports to senior management. Key Performance Indicators (KPIs): Project Delivery: Percentage of data engineering projects completed on time and within budget. System Uptime: Percentage of time data systems are operational and available to users. Data Quality: Reduction in data errors and anomalies, as measured by data validation checks and user feedback. Pipeline Performance: Improvement in data pipeline performance, including data ingestion and processing times. Team Productivity: Throughput and efficiency of the data engineering team in delivering solutions and resolving issues. Stakeholder Satisfaction: Feedback from stakeholders on the effectiveness and impact of data engineering solutions. Innovation Adoption: Rate of adoption and successful integration of new technologies and tools in the data engineering processes. Compliance: Adherence to data governance policies and regulatory compliance standards. Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. Proven experience (10 years) in data engineering or a related field, with at least Y years in a leadership or managerial role. Strong proficiency in data modeling, ETL processes, data warehousing, and big data technologies Expertise in cloud platforms (e.g., AWS, Azure, GCP) and database management systems. Excellent leadership, communication, and interpersonal skills. Demonstrated ability to manage multiple projects and prioritize tasks effectively. Strong problem-solving skills and a proactive approach to addressing challenges. Experience with data governance and compliance best practices. Preferred Qualifications: Experience with machine learning and advanced analytics. Certification in cloud platforms or data engineering technologies. Familiarity with data visualization tools (e.g., Tableau, Power BI). #LI-KD1 #LI-REMOTE #DBCORP