Technical Solutions Architect, Internal Enterprise Product Strategy

📍 Job Overview

Job Title: Technical Solutions Architect, Internal Enterprise Product Strategy

Company: Google

Location: Austin, TX; New York, NY; San Jose, CA

Job Type: Full-Time

Category: Revenue Operations / Sales Operations / GTM Strategy (Internal Enterprise Product Strategy with a focus on Data and AI Enablement)

Date Posted: July 21, 2026

Experience Level: Mid-Senior Level (8+ years)

Remote Status: On-site

🚀 Role Summary

• This role is pivotal in defining and scaling enterprise-wide architectural standards, reference models, and patterns within Google's Corporate Engineering, with a specific emphasis on Data and AI Enablement.

• It involves deep collaboration with engineering teams to embed modern data architecture and AI standards, unblocking complex technical hurdles, and driving the active adoption of these enterprise-wide initiatives.

• The position requires a strong focus on execution and enablement, acting as a technical bridge between centralized AI infrastructure and distributed engineering squads.

• Success hinges on defining and scaling horizontal frameworks and tooling strategies for Process Mapping and Mining across Corporate Engineering to enhance efficiency and operational insight.

📝 Enhancement Note: While the title suggests "Product Strategy," the detailed description clearly positions this role within Enterprise Architecture and Corporate Engineering, focusing on internal systems, data, and AI enablement. This aligns it closely with advanced operations strategy roles that leverage technology and data to optimize internal GTM and product development processes. The emphasis on "scaling enterprise-wide architectural standards" and "driving adoption" points to a need for strong operational execution and cross-functional influence, characteristic of senior operations professionals.

📈 Primary Responsibilities

• Partner closely with other enterprise architects to ensure data and AI standards seamlessly integrate with broader enterprise platforms, applications, and core infrastructure, fostering a cohesive and efficient operational ecosystem.

• Collaborate with pillar leads and engineering teams to embed established data and AI reference patterns into their localized system designs, ensuring alignment and scalability across the organization.

• Lead technical forums, architecture deep-dives, and workshops to elevate data engineering and AI competencies across Corporate Engineering, driving knowledge sharing and skill development.

• Review data and AI proposals, proactively identifying architectural fragmentation, and mitigating security threats or technical debt to maintain system integrity and operational efficiency.

• Serve as the technical bridge between centralized AI infrastructure and distributed pillar squads, unblocking architectural dependencies and driving the execution of enterprise-wide initiatives.

• Define architectural guidelines that ensure robust data privacy, compliance, metadata management, and model lineage standards are deeply integrated into all technical designs, safeguarding data integrity and regulatory adherence.

• Define, scale, and own the horizontal framework and tooling strategy for Process Mapping and Mining (e.g., Celonis, Signavio) across Corporate Engineering, enhancing visibility and optimization opportunities for internal processes.

📝 Enhancement Note: The responsibilities highlight a strong operational focus on standardization, process optimization, and technical enablement, which are core tenets of Revenue Operations and Sales Operations roles, particularly those focused on platform strategy and efficiency. The inclusion of Process Mapping and Mining tools like Celonis and Signavio is a direct indicator of an operations-centric approach to understanding and improving internal workflows.

🎓 Skills & Qualifications

Education:

• Bachelor's degree or equivalent practical experience in a relevant technical field, such as Computer Science, Engineering, or Information Technology. Experience:

• Minimum of 8 years of experience in technology, software engineering, or enterprise architecture, with a significant emphasis on data engineering or AI/ML systems.

• Proven experience designing and implementing data architecture frameworks, enterprise data warehouses/data lakes, or production-grade AI/ML pipelines.

• Demonstrated experience in developing solution architectures through system design techniques, including distributed systems, designing under constraints, simplicity, limitations, robustness, and tradeoffs. Required Skills:

• Data Engineering & AI/ML Systems: Deep understanding and practical experience in designing, building, and scaling data pipelines, data lakes, data warehouses, and AI/ML models.

• Enterprise Architecture: Expertise in defining and implementing architectural standards, reference models, and patterns for large-scale enterprise environments.

• System Design: Proficiency in system design techniques, including distributed systems, microservices, event-driven architectures, and understanding of tradeoffs.

• Cloud-Native Scalability: Experience designing solutions that leverage cloud-native principles for scalability, reliability, and efficiency.

• Data Governance & Compliance: Strong knowledge of data privacy, compliance, metadata management, and model lineage standards.

Preferred Skills:

• Experience with modern system design patterns (e.g., microservices, event-driven architectures, Zero Trust security models, cloud-native scalability) and their integration with large-scale enterprise portfolios.

• Experience developing architectural strategies across a portfolio of internal systems within a complex, highly regulated global organization of large-scale.

• Demonstrated ability to operate effectively within an established central enterprise architecture function, balancing individual pillar delivery and velocity with shared enterprise goals.

• Proficiency with Process Mapping and Mining tools such as Celonis or Signavio.

• Experience in technical leadership and influencing cross-functional teams.

📝 Enhancement Note: The emphasis on data engineering, AI/ML, and enterprise architecture, combined with a requirement for 8+ years of experience, positions this role at a senior level. The preferred skills, particularly those related to process mapping/mining and operating within a central enterprise architecture function, strongly suggest a need for candidates with a robust understanding of operational efficiency and internal process optimization, which are key aspects of advanced Revenue Operations and GTM strategy roles.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Solution Architecture Case Studies: Showcase examples of designing and implementing complex data architectures, data lakes, data warehouses, or production-grade AI/ML pipelines. Highlight the scale, technical challenges, and outcomes.

• System Design Documentation: Provide documentation or detailed descriptions of system design processes, demonstrating an understanding of distributed systems, robustness, tradeoffs, and design under constraints.

• Process Optimization Initiatives: Include projects where you defined, scaled, or owned frameworks for process mapping, mining, or workflow automation, preferably using tools like Celonis or Signavio. Quantify efficiency gains or insights derived.

• Architectural Standards & Frameworks: Present examples of architectural guidelines, reference models, or patterns you developed and implemented, particularly those related to data privacy, compliance, or AI model lineage.

Process Documentation:

• Workflow Design & Optimization: Detail how you've approached the design and optimization of complex workflows, especially those involving data lifecycle management or AI deployment.

• Implementation & Automation Methods: Illustrate your methods for implementing architectural standards and automating processes, including how you unblocked technical dependencies for engineering teams.

• Measurement & Performance Analysis: Show how you've measured the impact of architectural decisions and process improvements, focusing on metrics related to efficiency, scalability, security, and adoption.

📝 Enhancement Note: For a role focused on enterprise architecture and internal process optimization, a portfolio demonstrating concrete examples of system design, data architecture implementation, and process improvement initiatives is crucial. The inclusion of process mapping/mining tools and architectural standards highlights the need for candidates to showcase their ability to not only design but also operationalize and drive adoption of complex technical solutions within an enterprise context.

💵 Compensation & Benefits

Salary Range:

• Austin, TX / New York, NY / San Jose, CA: $183,000 - $266,000 USD per year.

Benefits:

• Performance Bonus: Target of 20% of base salary, reflecting the results-driven nature of the role and its impact on enterprise objectives.

• Equity: Potential for stock options or grants, aligning individual success with Google's overall growth and performance.

• Health Insurance: Comprehensive medical, dental, and vision coverage for employees and their dependents.

• Retirement Savings Plan: Access to 401(k) or similar retirement savings plans with potential company matching.

• Paid Time Off (PTO): Generous vacation, sick leave, and paid holidays.

• Professional Development: Opportunities for continuous learning, training, conferences, and certifications relevant to enterprise architecture, data, and AI.

• Wellness Programs: Resources and programs focused on employee well-being.

Working Hours:

• Standard full-time work schedule (approximately 40 hours per week), with flexibility expected to meet project deadlines and collaborate across time zones. On-site presence is required.

📝 Enhancement Note: The salary range provided is specific to the US and aligns with senior technical roles at major tech companies. The additional components like a target bonus and equity are standard for such positions, reflecting performance-based compensation. The emphasis on professional development and wellness is typical for Google and beneficial for operations professionals looking to stay current in a rapidly evolving tech landscape.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / Internet / Software & Services. Google operates at the forefront of technological innovation, impacting diverse sectors from advertising and cloud computing to AI and hardware.

Company Size: Extremely Large (Over 10,000 employees). This scale implies complex internal systems, established processes, and a significant need for standardized, scalable solutions.

Founded: 1998. With a long history of innovation, Google has developed robust internal frameworks and a culture that values technical excellence, data-driven decision-making, and continuous improvement.

Team Structure:

• Operations Focus: This role is within the Enterprise Product Strategy and Architecture (EPA) team, specifically focused on Data and AI Enablement within Corporate Engineering. This team acts as a central architectural function.

• Reporting Structure: Likely reports to a Director or Senior Manager of Enterprise Architecture or Corporate Engineering, with a matrixed reporting structure for project-specific initiatives.

• Cross-functional Collaboration: Extensive collaboration is expected with other enterprise architects, technical leads, engineering squads across various Corporate Engineering pillars, and potentially product management teams.

Methodology:

• Data-Driven Decision Making: Emphasis on leveraging data to define architectural standards, identify optimization opportunities, and measure impact.

• Agile & Iterative Development: While focused on enterprise architecture, the approach likely incorporates agile principles for rapid iteration, feedback, and adaptation of solutions.

• Standardization & Scalability: A core methodology involves defining and scaling reusable architectural patterns and frameworks to ensure consistency and efficiency across the organization.

Company Website: https://www.google.com

📝 Enhancement Note: Google's culture is known for its emphasis on data, engineering rigor, and innovation. For an operations-focused role within this environment, expect a high degree of technical scrutiny, a focus on measurable impact, and a collaborative yet performance-driven atmosphere. The scale of Google means that architectural decisions have broad implications, requiring a strategic and systematic approach to operations and technology implementation.

📈 Career & Growth Analysis

Operations Career Level: This position is classified as a senior-level Enterprise Architect, requiring significant experience and strategic impact. It sits at the intersection of technical architecture, product strategy, and operational enablement for internal corporate functions.

Reporting Structure: The role reports into a centralized Enterprise Architecture function within Corporate Engineering. This structure allows for broad influence across various engineering pillars and product development efforts.

Operations Impact: The role's impact is substantial, focusing on defining the technical foundation for data and AI capabilities that underpin Google's internal operations. This directly influences the efficiency, security, and innovation capacity of Corporate Engineering, which in turn supports all of Google's product development and business operations.

Growth Opportunities:

• Specialization: Deepen expertise in specific areas of data architecture, AI/ML systems, or process mining, becoming a recognized subject matter expert within Google.

• Leadership: Transition into technical leadership roles, managing architectural teams, or leading major enterprise-wide technology initiatives.

• Cross-functional Mobility: Leverage experience in Corporate Engineering to move into roles focused on product development architecture, cloud infrastructure, or advanced analytics within other Google divisions.

• Strategic Influence: Grow to influence broader enterprise technology strategy and roadmap development, shaping the future of Google's internal technology landscape.

📝 Enhancement Note: This role offers a clear path for growth within enterprise architecture and technical leadership. For operations professionals, it provides an opportunity to apply their strategic thinking and process optimization skills at a massive scale, influencing core technology decisions that drive operational efficiency and innovation across a global tech giant.

🌐 Work Environment

Office Type: On-site. This role requires a consistent presence at one of Google's major US office locations (Austin, New York, or San Jose).

Office Location(s):

• Austin, TX: Google's Austin campus is a major hub for engineering and operations.

• New York, NY: A vibrant office in the heart of Manhattan, known for its engineering and product teams.

• San Jose, CA: Located in Silicon Valley, this office is central to Google's core engineering and product development efforts.

Workspace Context:

• Collaborative Environment: Google offices are designed to foster collaboration, with ample meeting spaces, common areas, and team-oriented setups.

• Access to Tools & Technology: Employees have access to cutting-edge internal tools, robust infrastructure, and extensive technological resources necessary for complex architectural work.

• Team Interaction: Frequent opportunities for in-person interaction with cross-functional teams, including engineers, architects, product managers, and leadership, facilitating rapid problem-solving and knowledge exchange.

Work Schedule: The role operates on a standard full-time schedule, emphasizing on-site presence. While core hours are expected, the dynamic nature of enterprise architecture and global collaboration may necessitate some flexibility to accommodate project needs and team interactions across different time zones.

📝 Enhancement Note: The on-site requirement underscores the importance of in-person collaboration for this role, particularly for leading workshops, deep-dives, and fostering team cohesion within the enterprise architecture function. Google's office environments are typically well-equipped to support technical professionals.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review applications and conduct an initial call to assess basic qualifications and fit.

• Technical Phone Screens: Expect one or more technical interviews focused on system design, data architecture principles, AI/ML concepts, and problem-solving.

• On-site/Virtual Loop: A series of interviews (typically 4-5) covering various aspects:

  • System Design: In-depth scenarios requiring you to design scalable, robust systems.
  • Behavioral/Leadership: Questions assessing your experience, collaboration style, and ability to influence.
  • Domain Expertise: Interviews focusing on your specific experience with data engineering, AI/ML, or enterprise architecture.
  • Cross-functional Collaboration: Scenarios testing your ability to work with diverse teams and drive adoption.

• Portfolio Presentation: You may be asked to present specific case studies or examples from your portfolio to illustrate your experience and approach to architectural challenges.

Portfolio Review Tips:

• Focus on Impact: For each project, clearly articulate the problem, your solution, the technologies used, and the measurable business or operational impact (e.g., efficiency gains, cost savings, improved scalability, risk reduction).

• Showcase System Design Skills: Include examples that demonstrate your ability to design complex systems, detailing tradeoffs, constraints, and robustness considerations.

• Highlight Process Optimization: If you have experience with process mapping/mining or workflow automation, present case studies that show how you identified bottlenecks and implemented improvements.

• Tailor to Google's Context: Frame your experiences in a way that aligns with Google's culture of innovation, data-driven decision-making, and large-scale engineering.

Challenge Preparation:

• System Design Practice: Rehearse common system design questions (e.g., design Twitter's feed, design a URL shortener, design a distributed cache). Focus on breaking down the problem, defining requirements, identifying components, discussing scalability, and handling tradeoffs.

• Data Architecture Scenarios: Prepare for questions about designing data lakes, data warehouses, ETL/ELT processes, and managing data governance.

• AI/ML Pipeline Design: Be ready to discuss the lifecycle of an ML model, from data preparation to deployment and monitoring.

• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) answers for questions about leadership, conflict resolution, influencing stakeholders, and managing complex projects.

📝 Enhancement Note: The interview process at Google is rigorous, especially for technical roles. For an architect position, system design and problem-solving abilities are paramount. Emphasizing your ability to drive adoption and influence cross-functional teams will be critical, aligning with the operational aspects of this role.

🛠 Tools & Technology Stack

Primary Tools:

• System Design & Architecture: Tools for diagramming (e.g., Lucidchart, Draw.io, Visio), modeling, and potentially simulation.

• Process Mapping & Mining: Celonis, Signavio, or similar platforms for analyzing and optimizing business processes.

• Cloud Platforms: Deep familiarity with cloud-native architectures on platforms like Google Cloud Platform (GCP), AWS, or Azure, including services relevant to data engineering and AI/ML.

• Data Warehousing & Data Lakes: Experience with technologies like BigQuery, Snowflake, Redshift, Databricks, or similar data storage and processing solutions.

• AI/ML Platforms & Frameworks: Familiarity with ML frameworks (e.g., TensorFlow, PyTorch), MLOps tools, and cloud-based AI services.

Analytics & Reporting:

• Data Analysis Tools: SQL, Python (with libraries like Pandas, NumPy), R for data manipulation and analysis.

• Visualization Tools: Tools like Looker, Tableau, or internal Google equivalents for creating dashboards and reporting on architectural adoption and process efficiency.

CRM & Automation:

• While not directly CRM-focused, understanding how enterprise architecture impacts internal systems and workflows is key. Experience with workflow automation tools and understanding of API integrations for system interconnectivity will be beneficial.

• Zero Trust Security Models: Understanding principles and practical application of Zero Trust architectures.

📝 Enhancement Note: The tools mentioned, especially Celonis and Signavio, are directly related to process optimization and operational efficiency, reinforcing the operations-centric nature of this role. A strong command of cloud technologies, data platforms, and system design principles is essential.

👥 Team Culture & Values

Operations Values:

• Data-Driven Innovation: A strong emphasis on using data to inform architectural decisions, identify opportunities for process improvement, and measure the impact of solutions.

• Technical Excellence & Rigor: A commitment to high-quality engineering, robust design, and meticulous attention to detail in all architectural work.

• Collaboration & Influence: Valuing teamwork, open communication, and the ability to influence stakeholders across diverse teams to drive adoption of standards and best practices.

• Scalability & Efficiency: A focus on building solutions that are not only functional but also highly scalable, efficient, and cost-effective for a global organization.

• Continuous Learning: Encouraging a culture of ongoing learning and skill development to stay ahead of technological advancements in data, AI, and enterprise architecture.

Collaboration Style:

• Cross-functional Integration: Actively engaging with various engineering pillars and product teams to ensure alignment and seamless integration of architectural standards.

• Knowledge Sharing & Mentorship: A culture of sharing expertise through forums, workshops, and direct mentorship to uplift the technical capabilities of engineering teams.

• Feedback Loops: Establishing mechanisms for continuous feedback on architectural designs and implemented solutions to foster iterative improvement and adaptation.

• Strategic Partnership: Acting as a strategic partner to engineering leadership, providing architectural guidance that aligns with business objectives and operational priorities.

📝 Enhancement Note: Google's emphasis on data-driven decision-making and technical rigor aligns perfectly with the requirements of a high-impact operations role. The collaborative style expected, especially in influencing adoption, is a key trait for successful operations professionals who need to drive change across an organization.

⚡ Challenges & Growth Opportunities

Challenges:

• Driving Adoption at Scale: Convincing and enabling numerous engineering teams across a vast organization to adopt new architectural standards and best practices can be a significant hurdle.

• Balancing Centralization vs. Decentralization: Navigating the tension between establishing global enterprise standards and allowing for localized flexibility and innovation within individual engineering pillars.

• Keeping Pace with Technology: The rapid evolution of AI, ML, and data technologies requires continuous learning and adaptation of architectural strategies.

• Technical Debt Management: Identifying and mitigating existing technical debt while simultaneously introducing new, advanced architectural patterns.

• Ensuring Data Privacy & Compliance: Integrating stringent data privacy and compliance requirements into complex, large-scale data and AI systems.

Learning & Development Opportunities:

• Advanced Specialization: Opportunities to deepen expertise in cutting-edge areas of AI/ML, data governance, distributed systems, and cloud-native architectures.

• Industry Conferences & Certifications: Support for attending leading industry events and obtaining certifications relevant to enterprise architecture, cloud computing, and data science.

• Mentorship Programs: Access to mentorship from senior architects and technical leaders within Google, fostering career growth and leadership development.

• Internal Training & Workshops: Extensive internal resources for skill development, including specialized training on Google's internal tools and platforms.

• Exposure to Diverse Projects: Working on a wide array of internal product strategies and infrastructure projects, offering broad exposure to different technical challenges and business needs.

📝 Enhancement Note: The challenges highlight the strategic and operational complexities of working in enterprise architecture at Google. The growth opportunities are substantial, offering a clear path for individuals to become leaders in their technical domains and influence the direction of technology within one of the world's largest tech companies.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to influence a team to adopt a new architectural standard or technology. What was your approach, and what was the outcome?"

  • Preparation: Focus on your ability to communicate technical value, build consensus, and address concerns. Use the STAR method, highlighting your strategic approach to adoption and the measurable impact.

• "How would you design a scalable and secure data pipeline for processing sensitive user data across multiple regions, considering privacy regulations?"

  • Preparation: Outline your system design process: requirements gathering, component selection (e.g., data ingestion, processing, storage, security layers), scalability considerations, and how you'd incorporate data privacy and compliance checks. Mention specific technologies or patterns.

• "Imagine you've identified significant architectural fragmentation in a critical internal system. What steps would you take to address it, and how would you prioritize remediation efforts?"

  • Preparation: Discuss your methodology for assessing fragmentation, engaging stakeholders, proposing standardized solutions, and prioritizing based on risk, impact, and feasibility. Emphasize collaboration and iterative improvements.

Company & Culture Questions:

• "Why are you interested in Google's Enterprise Architecture function, and how do your skills align with our focus on Data and AI Enablement?"

  • Preparation: Research Google's technical blogs, recent AI/data announcements, and the specific team's charter. Connect your experience directly to their stated goals and challenges.

• "How do you balance the need for enterprise-wide standards with the desire for individual teams to innovate quickly?"

  • Preparation: Discuss your understanding of architectural governance, reference architectures, and how to create frameworks that provide guardrails while enabling agility. Mention your experience with agile methodologies.

• "Google is known for its data-driven culture. How do you leverage data to inform your architectural decisions and measure the success of your initiatives?"

  • Preparation: Provide specific examples of how you've used data analysis, metrics, or process mining to justify architectural choices, identify issues, or demonstrate ROI for your projects.

Portfolio Presentation Strategy:

• Structure Your Case Studies: For each project, clearly present the problem, your role and approach, the technical solution (including architecture diagrams if possible), the challenges encountered, the results achieved (quantified impact), and lessons learned.

• Highlight Process Optimization: If showcasing process mapping/mining, clearly articulate the "before" and "after" states, the tools used, and the quantifiable improvements in efficiency, cost, or throughput.

• Emphasize Scalability & Robustness: For system design examples, focus on how you addressed scalability, reliability, fault tolerance, and security.

• Be Ready for Deep Dives: Anticipate detailed questions about your technical decisions, tradeoffs, and alternatives considered. Be prepared to defend your choices with sound reasoning.

📝 Enhancement Note: Preparing for Google interviews requires a deep understanding of system design, data architecture, and the ability to articulate your thought process clearly. For this role, demonstrating an operational mindset—how you drive adoption, optimize processes, and ensure efficiency—will be as important as your technical prowess.

📌 Application Steps

To apply for this operations-centric Technical Solutions Architect position:

• Submit your application through the Google Careers portal link provided.

• Curate Your Operations Portfolio: Select 2-3 key projects that best showcase your experience in enterprise architecture, data/AI systems, and process optimization. For each, prepare a concise summary highlighting the problem, your solution, the technologies used, and the quantifiable operational impact (e.g., efficiency gains, cost reduction, improved scalability).

• Optimize Your Resume for Operations & Architecture Keywords: Ensure your resume clearly lists experience with "Data Engineering," "AI/ML Systems," "Enterprise Architecture," "System Design," "Process Mapping," "Process Mining," "Scalability," "Data Governance," and relevant tools (e.g., Celonis, Signavio, GCP). Use action verbs and quantify achievements wherever possible.

• Practice System Design & Behavioral Questions: Rehearse system design scenarios and prepare STAR method answers for behavioral questions, focusing on your ability to influence, collaborate, and drive adoption of technical standards and processes within large organizations.

• Research Google's Enterprise Architecture & Culture: Understand Google's approach to internal technology, data governance, and AI. Familiarize yourself with their stated values and how they impact engineering and operations.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.