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Google Cloud Generative AI Solution Lead - Senior Manager

PwC Acceleration Centres · Bangalore, India

About this role

The Google Cloud Generative AI Solution Lead - Senior Manager will join the Acceleration Center India to guide client-facing data engineering efforts focused on Google Cloud and generative AI solutions. This role involves shaping data pipelines, integration approaches, and transformation work to support actionable insights within the Data and Analytics Engineering practice. As a Senior Manager, you will lead complex programs, coach teams through ambiguity, and translate business needs into clear technical direction. Responsibilities: Lead Google Cloud generative AI solution design and delivery for data modernization initiatives. Advise client stakeholders on generative AI use cases, platform strategy, and implementation. Translate business needs into technical architectures across data pipelines, integration, and analytics layers. Direct teams through complex delivery efforts involving data engineering and AI integration. Coordinate data modeling, quality, validation, and performance tuning. Guide AI fluency, agile scalability, and rapid experimentation. Coach team members on technical direction and delivery priorities. Validate technical outcomes with client leaders and adjust designs based on feedback. Required Qualifications: Bachelor's degree. At least 14 years of experience. Oral and written proficiency in English. Preferred Qualifications: Degree in Management Information Systems, Computer and Information Science, Systems Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, or Statistics. Proven experience in Google Cloud generative AI solution delivery, data modernization methods, executive-level communication, and directing cross-functional teams through large-scale technical programs.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Google Cloud PlatformGenerative AIData EngineeringData ModernizationCloud Solution DesignData PipelinesData AnalyticsAgile MethodologyTechnical ArchitectureStakeholder Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Google Cloud Generative AI Solution Design
The primary focus is leading the end-to-end design and delivery of generative AI solutions on Google Cloud. You must translate complex business requirements into robust technical architectures, specifically focusing on data pipelines, integration, and transformation layers to support data modernization initiatives.
Executive Stakeholder Advisory
You are expected to act as a trusted advisor to client stakeholders. This involves translating high-level business needs into clear technical direction and communicating complex AI platform strategies and implementation approaches to non-technical leadership.
Cross-Functional Team Leadership and Coaching
As a Senior Manager, you must direct large-scale technical programs and coach cross-functional teams through ambiguity. This includes guiding team members on technical priorities, issue resolution, and maintaining high standards for data quality, modeling, and performance tuning.

Likely challenges to think through

Balancing Rapid Experimentation with Scalable Delivery
Generative AI projects often require rapid experimentation, which can conflict with the need for stable, scalable data modernization. Prepare to discuss how you manage the trade-off between agile prototyping and the rigorous engineering standards required for enterprise-grade production environments.
Navigating Ambiguity in Client Engagements
Client requirements for AI solutions are often ill-defined or evolving. You will face the challenge of providing clear technical direction while managing shifting expectations. Prepare examples of how you have used sound judgment to steer teams through uncertain project phases while maintaining alignment with business objectives.

Practice your answers

Technical practice

Designing Generative AI Architectures on Google Cloud

What to coverExplain how you integrate Vertex AI with existing data pipelines. Discuss the trade-offs between using pre-trained models versus fine-tuning for specific client use cases, focusing on scalability and cost-efficiency.

Data Modernization Strategies

What to coverDescribe your approach to migrating legacy data environments to Google Cloud. Highlight how you ensure data quality, lineage, and performance tuning during the transition to modern cloud-native warehouses.

AI Integration and Data Pipelines

What to coverHow do you architect data pipelines to support real-time AI inference? Address the challenges of data ingestion, transformation, and ensuring low-latency access for generative AI models.

Ensuring Data Quality and Validation

What to coverWhat frameworks do you use to validate data quality in complex AI-driven solutions? Explain how you maintain high standards for data integrity when dealing with unstructured data inputs for generative models.

Agile Scalability in AI Projects

What to coverHow do you apply agile methodologies to manage rapid experimentation in generative AI? Discuss how you balance the need for iterative development with the requirement for robust, production-ready technical architectures.

Performance Tuning for Cloud Analytics

What to coverDescribe your methodology for identifying and resolving performance bottlenecks in large-scale Google Cloud data environments. Focus on optimization techniques for both storage and compute layers.

Platform Strategy and Tool Selection

What to coverHow do you advise stakeholders on selecting the right Google Cloud tools for their specific AI and data needs? Suggestion: Prepare to discuss the comparative advantages of BigQuery, Dataflow, and Vertex AI components.

Translating Business Needs to Technical Architecture

What to coverProvide an example of how you have translated ambiguous business requirements into a concrete technical roadmap. Explain how you ensure the architecture remains aligned with long-term business objectives.

Behavioural practice

Leading Through Ambiguity

What to coverDescribe a time you led a team through a highly ambiguous project phase. Focus on how you provided clear technical direction and maintained team morale while navigating uncertainty.

Managing Executive Stakeholders

What to coverGive an example of how you communicated complex AI technical concepts to non-technical executive stakeholders. Explain how you tailored your message to gain their buy-in and address their specific concerns.

Coaching and Mentoring Teams

What to coverDescribe your approach to coaching cross-functional teams on technical delivery priorities. Provide an example of how you helped a team member overcome a significant technical or process-related challenge.

Handling Feedback and Iteration

What to coverTell us about a situation where client feedback required a significant change to your proposed solution design. How did you manage the pivot while maintaining project timelines and quality standards?

Directing Large-Scale Programs

What to coverDiscuss a time you managed a large-scale technical program with multiple workstreams. How did you coordinate efforts, manage dependencies, and ensure operational excellence across the engagement?

Sharpen your knowledge

Topics to prepare

Generative AI Platform Strategy
Review Google Cloud's generative AI ecosystem, specifically Vertex AI and its integration with data modernization workflows. Exercise: Draft a high-level strategy document outlining how to transition a legacy data warehouse to a generative AI-ready architecture on Google Cloud, focusing on scalability and business value.
Data Modernization Frameworks
Understand the lifecycle of moving on-premise or siloed data into cloud-native environments. Exercise: Map out a technical roadmap for a hypothetical client migration, identifying key stages like data ingestion, quality validation, and performance tuning within the Google Cloud ecosystem.
Executive Stakeholder Communication
Focus on translating complex technical AI architectures into business-centric narratives. Exercise: Prepare a 5-minute pitch explaining the ROI of a generative AI implementation to a non-technical stakeholder, emphasizing risk mitigation and operational efficiency.

Skills to revise

Google Cloud Architecture Design
Practice designing end-to-end solutions using Google Cloud services. Exercise: Create a diagram for a data pipeline that integrates generative AI models with existing analytics layers, ensuring data quality and security standards are met.
Agile Program Leadership
Develop techniques for managing cross-functional teams through ambiguity. Exercise: Simulate a sprint planning session where you must prioritize competing technical requirements for a large-scale AI delivery project while coaching junior team members.
Data Engineering and Integration
Refine skills in building and maintaining scalable data pipelines. Exercise: Review best practices for data modeling and performance tuning in Google Cloud, specifically addressing how to handle large-scale datasets for AI model training and inference.

Your preparation checklist

Map Google Cloud GenAI capabilities to data modernization
Prepare a technical framework demonstrating how you integrate Google Cloud AI services (such as Vertex AI) with traditional data pipelines and modern warehouse architectures to solve specific business problems.
Develop an executive-level communication strategy
Create a summary presentation or case study that explains a complex AI-driven data transformation project to non-technical stakeholders, focusing on business value, platform strategy, and risk mitigation.
Structure a leadership approach for cross-functional teams
Outline your methodology for coaching teams through ambiguity, managing large-scale technical delivery, and resolving conflicts within agile environments to ensure project milestones are met.
Prepare evidence of large-scale program delivery
Document specific examples from your 14+ years of experience where you directed complex data engineering programs, highlighting your role in data modeling, quality validation, and performance tuning.
Formulate strategic questions for the interview
Prepare insightful questions regarding the firm's current approach to AI fluency, how they balance rapid experimentation with enterprise-grade scalability, and the primary challenges they face in client-facing data modernization engagements.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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