Solutions Architect, Financial Services, Google Cloud

About The Job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Solutions Architect for Financial Services, you will be at the forefront of the AI revolution in banking, insurance, and capital markets. You will combine your technical mastery of Vertex AI, Gemini Enterprise, BigQuery and other Google Cloud offerings with an understanding of financial industry workflows. Your role requires a balance of high-level architectural thinking and technical execution to ensure our most customers build sustainable, data-driven futures on Google Cloud.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Minimum Qualifications

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in architectural design and deployment of cloud-based solutions, utilizing BigQuery for data warehousing and analytics.
• Experience in implementing machine learning workflows or generative AI solutions using Vertex AI or similar cloud-native AI platforms.
• Experience working within the Financial Services sector (e.g., banking, insurance, or capital markets).
• Experience in customer-facing role advising stakeholders on data modernization strategies and the integration of AI into core business processes.

Preferred Qualifications

• Experience in systems design with ability to architect and explain data analytics pipelines and data flows.
• Experience designing and deploying with one or more from the following technologies: TensorFlow, Spark ML, CNTK, Torch, Caffe, Scikit-learn.
• Experience in a statistical programming language like R or Python and applied machine learning techniques (e.g., dimensionality reduction strategies, classification, and natural language processing frameworks).
• Understanding of industry-specific data security or regulatory compliance requirements.
• Excellent communication, presentation and problem-solving skills.

Responsibilities

• Serve as a trusted technical advisor to executive stakeholders at major financial institutions, guiding them through the adoption of Google Cloud AI and data and analytics ecosystems.
• Architect and deploy end-to-end solutions using Vertex AI, Generative AI models, BigQuery and other Google Cloud platform (GCP) offerings to solve financial use cases such as fraud detection, personalized banking, and automated regulatory reporting.
• Lead customers in migrating legacy data warehouses to BigQuery and establishing modern data governance frameworks that ensure security and compliance within the financial regulatory environment.
• Develop proofs-of-concept that showcase the engaged advantages of Google’s AI tools, helping customers realize immediate value from their data assets.
• Partner closely with Business, Product Engineering, and Professional Services teams to align customer requirements with the Google Cloud product roadmap, advocating for features in the AI and analytics space.

Key skills/competency

• Cloud Architecture
• BigQuery
• Vertex AI
• Financial Services
• Data Modernization
• AI Integration
• Machine Learning
• Generative AI
• Data Governance
• Stakeholder Advisory