Sr Manager, Data Engineering

McDonald's Corporation•Chicago, IL

About The Position

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. We are looking for a Sr. Manager, Data Engineering – Data Clean Rooms to lead the strategy, delivery, and evolution of privacy‑safe data collaboration capabilities across the enterprise. In this role, you will be responsible for defining and scaling data clean room platforms that enable secure analytics, measurement, and insights while adhering to global privacy and data governance standards. You will work closely with product, data engineering, privacy, legal, security, analytics, and business teams to translate complex use cases into robust, scalable clean room solutions. This role blends strong technical acumen with platform thinking and stakeholder leadership to ensure data can be responsibly shared and activated to drive meaningful business impact.

Requirements

7–10+ years of experience in product management, data platforms, or analytics products, with ownership of complex, enterprise‑scale products.
Strong background in Python‑based data platforms and services, with the ability to guide design and code quality through senior engineers.
Familiarity with Data Clean Rooms or secure data collaboration platforms, including concepts such as controlled joins, aggregation rules, and restricted output delivery.
Google Cloud Platform (GCP) certification (e.g., Professional Data Engineer, Professional Cloud Architect, or equivalent), demonstrating hands‑on expertise in designing, building, and operating cloud‑native data platforms at scale.
Experience with event‑driven and API‑based data integration, including hands‑on use of Apache Kafka and Gravitee for secure data ingress/egress, partner data onboarding and activation/measurement.
Strong analytical skills, including defining and tracking product KPIs related to adoption, usage, performance, and business impact.
Excellent communication and stakeholder management skills, including the ability to present product strategy, tradeoffs, and outcomes to senior and executive audiences.

Nice To Haves

Industry experience in advertising, marketing analytics, retail media, measurement, or personalization.

Responsibilities

Own the product vision, strategy, and roadmap for Data Clean Room capabilities that enable secure, privacy‑compliant data collaboration across internal teams and external partners.
Translate business and analytical needs into clear, well‑defined product requirements, partnering closely with engineering to deliver scalable and reliable solutions.
Lead a data engineering team that collaborates with architecture, privacy, legal, and security teams to design and evolve DCR platform capabilities, including secure data access, identity abstraction, and controlled analytics workflows.
Guide technical decision‑making by helping teams balance scalability, performance, privacy, cost, and usability considerations.
Define standards and guardrails for data quality, access controls, monitoring, and auditing within DCR environments.
Drive integration with adjacent platforms such as data foundations, identity, consent management, analytics, and activation systems.
Evaluate and support build vs. buy decisions, working with cloud providers and technology partners to assess fit against enterprise requirements.
Enable analytics, measurement, and media use cases by partnering with data science and analytics teams to support privacy‑safe insights, measurement, and attribution.
Establish and track product success metrics, including adoption, performance, platform reliability, and business impact.
Communicate product plans, system constraints, and progress clearly to technical and non‑technical stakeholders, ensuring alignment and transparency across teams.