Senior Staff Business Systems Analyst, Data Architecture, Spaces Engineering

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 12 years of experience building integrated systems throughout the entire lifecycle (e.g., analyze, design, build, test, implement, and support).
• 12 years of experience designing and implementing data systems.
• Experience with Artificial Intelligence/Machine Learning concepts and their specific data requirements.

Preferred qualifications:

• Experience in real estate data and associated business processes, including strategic planning, the property life-cycle, and financial considerations.
• Knowledge of geospatial data and its applications within the real estate sector.

About the job

At Google, we work at lightning speed. So when things get in the way of progress, the Business Systems Integration team steps in to remove those roadblocks. The team identifies time-consuming internal processes and then builds solutions that are reliable and scalable enough to work within the size and scope of the company. You listen to and translate Googler needs into high-level technical specifications, design and develop recommended systems and consult with Google executives to ensure smooth implementation. Whether battling large system processes or leveraging our homegrown suite of Google products for Googlers themselves, you help Googlers work faster and more efficiently.

With your technical expertise, you build business applications and influence the design of features that improve the reliability of our applications and systems. You also define the scope of technical projects and oversee execution.

As a Senior Data/Information Architect in the Spaces Engineering team, you will lead the comprehensive design and implementation of data infrastructure solutions, specifically for Google’s Real Estate and Workplace Services (REWS) IT portfolio. You will be instrumental in developing and driving the data strategy for implementing and maintaining an AI-ready data landscape, ensuring our data foundation seamlessly supports all facets of our operations, from high-level strategic portfolio planning and the integration of third-party systems’ data to the advanced demands of our forthcoming AI/ML initiatives. You will possess an understanding of enterprise data management, data governance, and support the foundational data and automation needs of business units from within a corporate IT function. You will collaborate closely with business stakeholders, IT teams, and external partners.

At Corp Eng, we build world-leading business solutions that scale a more helpful Google for everyone. As Google’s IT organization, we provide end-to-end solutions for organizations across Google. We deliver the right tools, platforms, and experiences for all Googlers as they create more helpful products and services for everyone. In the simplest terms, we are Google for Googlers.

The US base salary range for this full-time position is $221,000-$311,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Develop and maintain the enterprise data architecture strategy for Google's Real Estate portfolio, ensuring alignment with overarching business objectives, IT strategies, and, critically, our AI-ready roadmap.
• Design and implement scalable, high-performance data models for various real estate functions, including portfolio planning, asset management, property management, and financial analysis, with a focus on optimizing data for AI/ML consumption.
• Lead the integration of data sources, encompassing internal systems, external market data, and third-party real estate platforms, ensuring data cleanliness, consistency, and structural integrity for advanced analytics.
• Define and enforce data governance standards, guaranteeing data quality, security, and compliance across all real estate data assets, with particular emphasis on preparing data for AI model training and deployment.
• Provide technical leadership and mentorship across the organization. Foster a culture of data excellence and proactive AI preparedness.