Senior Manager, Data Engineering

Company Description

McDonald’s growth strategy, Accelerating the Arches, encompasses all aspects of our business as the leading global omni-channel restaurant brand. As the consumer landscape shifts we are using our competitive advantages to further strengthen our brand. One of our core growth strategies is to Double Down on the 3Ds (Delivery, Digital and Drive Thru). McDonald’s will accelerate technology innovation so 65M+ customers a day will experience a fast, easy experience, whether at one of our 25,000 and growing Drive thrus, through McDelivery, dine-in or takeaway. 

McDonald’s Global Technology is here to power tomorrow’s feel-good moments.That’s why you’ll find us at the forefront of transformative technology, exploring new and innovative ways to serve our millions of customers and spread happiness one delicious Hot Fudge Sundae-dipped fry at a time. Using AI, robotics and emerging tech, we’re digitizing the Golden Arches. Combine that with our unparalleled global scale, and we’re reshaping all areas of the business, industry and every community that is home to a McDonald’s restaurant. We face complex tech challenges every day. But that’s where our diverse and talented teams come in. They’re made up of the best and brightest from all over the globe, and they thrive in the space where feel-good meets fast-paced.  

Check out the McDonald’s  Global Technology Technical Blog to learn how technology and our global team are directly enabling the Accelerating the Arches strategy. 

Job Description

McDonald’s Global Technology – Data & Analytics team is looking to hire an Enterprise Data Analytics & AI (EDAA) Senior Manager, Data Engineering who can lead the solution, design and development of a new universal semantic layer tool for McDonald’s. As a Senior Manager, Data Engineering you will be responsible for designing the solution, managing the engineering squad to build the solution, and partnering with the business product owner to deploy and drive adoption in the markets and business functions. The semantic layer tool will help drive the standardization of key metrics and KPIs, drive better access to data, and enable easier data blending across our lake. Consumers of the semantic layer include business users, tech teams and other applications. To achieve this, you will work closely with the business product team and collaborate with other cross functional teams like enterprise and solution architecture, data governance, insights, and more. Your expertise in data engineering, analytics, AI, and BI architecture will play a crucial role in delivering a high-quality data semantic layer product and enabling one McDonald’s way of data-driven decision-making.

Responsibilities:

• Leads the design and architecture of the semantic layer solution to enable scalable, efficient data access for business users, applications, insights users, and tech teams
• Works with cross functional teams like solution architecture, enterprise architecture, data governance, cybersecurity, and functional stakeholders to ensure the semantic layer solution meets all priority functional and nonfunctional requirements
• Works with business product owner, data governance, product and insights teams to leverage the semantic layer to drive standardization of KPIs and metrics to establish One McDonald’s way of measurement
• Manages a data engineering squad to develop the MVP solution and future enhancements and iterations
• Drives key design decisions for the full stack semantic layer solution throughout design and build, coordinating between different stakeholders and leaders for input
• Collaborates with business product owner, in two in the box model, to prioritize work and manage product roadmap
• In collaboration with business product owner, engages with market stakeholders to identify how the semantic layer solution can help solve their business problems
• Establishes and maintains a solid understanding of the technical details of all data domains and clearly understands what business problems are being solved and capabilities enabled with their data
• Works with product and platform teams to integrate with the semantic layer, determining the right patterns to leverage
• Advocates for self-service analytics by enabling non-technical users to easily access and analyze data
• Collaborates with AI team to ensure the semantic layer enables key AI use cases (I.e., Agentic AI, NLQ queries, ML-driven insights)
• Manages integrations with other products and tools, establishing data contracts and core PDR principles
• Works with data products and architecture teams to maintain standardized data models to provide a consistent view across domains and products
• Leads solutioning of real-time and batch processing strategies to balance fre