Agentic Data Cloud Database Engineer, Google Cloud
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Oct 17, 2026. Google scores B on the Alion truth index.
About the job
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
The Agentic Data Cloud Databases Engineer guides customers through designing, migrating, and optimizing intelligent, multi-model database infrastructure built for the agentic era. You will work closely with enterprise customers to architect scalable, always-on transactional databases that serve as the contextual "brain" and real-time execution engine for AI agents. By leveraging automated onboarding, testing, migration, and observability database agents, you will de-risk the customer modernization journey and deliver paved, secure paths to enterprise-wide AI innovation.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $183000 - $265000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design and manage unified, multi-model database solutions (including relational, key-value, graph, and vector search engines). Deploy MCP servers to expose databases as secure, standardized interfaces for AI agents.
- Lead complex database modernizations using migration Agents. Standardize source-to-destination transfers, utilizing tools like the Database Migration Service (DMS).
- Shift database SRE to proactive, AI-driven management. Utilize the Gemini-powered database center and the database observability agent to consume real-time telemetry, automate workload simulations, perform root-cause analysis, and apply remediations.
- Implement QueryData APIs and database-specific contexts to build conversational analytics experiences, translating natural language business queries into SQL with high accuracy.
- Establish security protocols, encryption, and access control policies (IAM) aligned with industry regulations and Google Cloud best practices.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science, Information Systems, a related technical field, or equivalent experience.
- 8 years of experience with Google Cloud Databases (e.g., AlloyDB, Cloud SQL, Spanner, Firestore, and Bigtable).
- Experience with RDBMS such as PostgreSQL, Oracle, and SQL Server (e.g., engines, schema design, and concurrency).
- Experience in application development using Java, Python, build systems, ORMs (e.g., Hibernate), and JDBC.
- Experience with IaC and AI, including Terraform provisioning and AI integration via LangChain and LangGraph.
- Experience with performance and HA/DR (e.g., diagnosing execution plans, wait states, indexing, connection pooling, replication, automated failover, multi-region DR, and point-in-time recovery).
Preferred qualifications:
- Master’s degree in Engineering, Computer Science, Business, or a related field.
- Certifications: Professional Cloud Database Engineer.
- Experience using Gemini analytics and Observability Agents to diagnose plans, resolve contentions, optimize pooling, and benchmark.
- Experience managing hybrid/multi-cloud data environments or migrating to Google Cloud.
- Knowledge of AI-native database design, combining RDBMS expertise with vector and graph technologies, and tying databases into modern pipelines (e.g., Dataflow, CDC).
- Ability to design agent architectures (routing, state management) and integrate databases via MCP Toolbox for secure endpoints.