Schedule: Full Time

Job Type: Hybrid

Salary Type: Salary

Req #: 3143

About the Role

We are seeking a highly experienced Staff Data Products Engineer & Data Architect to lead the design, development, governance, and evolution of enterprise-scale data products and modern data architecture platforms. This role combines deep expertise in Data Product Engineering, Data Architecture, Google Cloud Platform (GCP), and modern data management principles to enable data-driven decision making and AI-powered business capabilities.

As a senior technical leader, working out of our Frisco office you will define architectural standards, establish data product strategies, drive cloud modernization initiatives, and mentor engineering teams in building scalable, secure, reliable, and reusable data assets. You will collaborate across business, engineering, analytics, AI/ML, and platform teams to deliver high-value data products that accelerate innovation and business growth. You will lead critical evaluations of new technologies and drive the architectural decisions that bridge the gap between data and complex business needs.

What the candidate will do
Data Product Strategy & Engineering

• Lead the design, development, and lifecycle management of enterprise data products using product-oriented operating models.
• Define and implement reusable, scalable, and governed data products across business domains.
• Establish standards for data product discoverability, ownership, interoperability, and consumption.
• Partner with business stakeholders to translate business requirements into scalable data solutions.
• Drive adoption of Data Mesh, Data Fabric, and domain-driven data ownership frameworks.
• Define KPIs and success metrics for data product adoption, quality, reliability, and business value realization.

Enterprise Data Architecture

• Develop and maintain enterprise data architecture roadmaps aligned with strategic business objectives.
• Design modern cloud-native architectures supporting batch, streaming, real-time, and AI-driven workloads.
• Create architecture standards, reference models, patterns, and best practices.
• Establish enterprise data modeling strategies including conceptual, logical, and physical models.
• Lead architecture reviews and recommend improvements in scalability, performance, security, and cost optimization.
• Ensure architecture alignment across data engineering, analytics, AI, and application teams.

Cloud Data Platform Leadership

• Architect and optimize enterprise data platforms in Google Cloud
• Establish cloud-native architecture patterns focused on scalability, resiliency, security, and automation.
• Lead cloud modernization and migration initiatives from legacy data platforms.

AI & Advanced Analytics Enablement

• Design AI-ready data architectures supporting Machine Learning, Generative AI, and advanced analytics initiatives.
• Partner with Data Science, AI Engineering, and Platform teams to support model development and operationalization.
• Build architectures supporting large-scale semantic, structured, and unstructured data processing.
• Evaluate emerging AI technologies and provide strategic recommendations.

Data Governance & Quality

• Establish enterprise-level data governance, metadata, lineage, quality, and compliance standards.
• Define data contracts, data product SLAs, and quality frameworks.
• Drive implementation of data observability, monitoring, and reliability practices.
• Ensure compliance with regulatory and organizational security requirements.
• Collaborate with security and compliance teams to implement data protection strategies.

Technical Leadership

• Act as a trusted advisor to executive leadership and senior stakeholders.
• Provide technical leadership across multiple engineering teams.
• Lead architecture review boards and technical design sessions.
• Mentor senior engineers, architects, and data product teams.
• Create engineering standards, implementation frameworks, and operational best practices.
• Drive innovation through proof-of-concepts and technology evaluations.

Basic Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or related field.
• 10+ years of experience in Data Engineering, Data Architecture, or related disciplines.
• 5+ years designing and implementing cloud-native enterprise data platforms.
• Proven experience building large-scale Data Products and Data-as-a-Service capabilities.
• Experience leading enterprise architecture initiatives across multiple business domains.

Preferred Qualifications

• Master's degree in Computer Science, Information Systems, Engineering, Data Science, or related field.
• Google Professional Data Engineer Certification.
• Google Professional Cloud Architect Certification.
• Experience with Generative AI implementation at enterprise scale.
• Experience designing multi-cloud and hybrid-cloud architectures.
• Hands-on experience with Data Mesh and modern data product operating models.
• Experience implementing enterprise metadata and governance platforms.
• Experience supporting AI, machine learning, and advanced analytics initiatives in large organizations.

Benefits & Compensation for U.S. Employees

Employees working more than 30 hours in the US at Uber Freight are eligible for benefits like a company sponsored health plan, dental and vision benefits, 401k match, financial and mental wellness benefits, parental leave, short- and long-term disability coverage, life insurance and more. US based employees may also be eligible for a performance or sales incentive bonus program, participation in Uber Freight equity awards, and other types of compensation depending upon the role.

For Chicago-based roles: The salary range for this role is $157,000.00 - $192,000.00 per year