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
Role Overview:
We are seeking a dedicated and detail-oriented Data & Analytics Engineering Production Support Manager to join our team. This is a hands on 24/7 role. In this role, you will be responsible for maintaining and supporting the data and analytics infrastructure, ensuring high availability, performance, and reliability of data services. You will work closely with data engineers, analysts, and business stakeholders to troubleshoot and resolve issues, optimize performance, and implement best practices for data operations.
Key Responsibilities
Monitoring and Maintenance: Monitor data pipelines, ETL processes, and analytics platforms to ensure smooth and efficient operations. Perform regular maintenance and updates to keep systems running optimally.
Incident Management: Quickly diagnose and resolve data-related issues and incidents. Implement root cause analysis and take corrective actions to prevent recurrence.
Performance Tuning: Analyze and optimize the performance of data systems, including databases, data warehouses, and analytics tools, to meet business requirements.
Data Quality Assurance: Conduct data quality checks and validations to ensure data accuracy, consistency, and integrity across all systems.
Automation: Develop and implement automation scripts and tools to streamline routine tasks, enhance system reliability, and improve operational efficiency.
Documentation: Maintain comprehensive documentation of data workflows, processes, and support activities. Create user guides and training materials for internal stakeholders.
Collaboration: Work closely with data engineers, data scientists, and business analysts to understand their needs and provide effective support. Communicate effectively with stakeholders to manage expectations and provide updates on issue resolution.
Continuous Improvement: Identify opportunities for process improvements and contribute to the enhancement of data and analytics support practices.
Key Performance Indicators (KPIs)
Incident Resolution Time: Average time taken to resolve data-related incidents and issues.
System Uptime: Percentage of time data and analytics systems are operational and accessible to users.
Data Quality: Percentage of data quality issues detected and resolved within defined SLAs.
Automation Coverage: Percentage of routine tasks and processes automated to reduce manual intervention.
Performance Metrics: Improvement in performance metrics of data systems, such as query response time and data processing speed.
User Satisfaction: Feedback from users and stakeholders on the responsiveness and effectiveness of support provided.
Documentation Completeness: Extent and quality of documentation for data workflows, processes, and support activities.
Support Ticket Volume: Number and types of support tickets resolved within a specified time frame.
Qualifications
Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field.
Proven experience (10+ years) in a leadership role in data engineering or related field.
Strong knowledge of database management systems, data warehousing, and ETL processes.
Proficiency in SQL, Python, or other scripting languages used for data manipulation and automation.
Familiarity with big data technologies, cloud platforms (e.g., AWS, Azure, GCP) & visualization tools (Qlik, Tableau, Power BI).
Excellent problem-solving skills and the ability to troubleshoot complex data issues.
Strong communication and collaboration skills to work effectively with cross-functional teams.
Detail-oriented with a commitment to maintaining high data quality and operational standards.
Preferred Qualifications
Experience with monitoring and logging tools
Certification in cloud platforms or data engineering technologies.
Knowledge of data governance and compliance standards.
This role is critical to ensuring the reliability and performance of our data and analytics infrastructure, enabling the business to make informed decisions based on accurate and timely data insights.
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Project Management and Information Technology
Industries
Motor Vehicle Manufacturing
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