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GCP Cloud ArchitectGeminiDatabase platform Engineer

Megan Soft Inc

Job Location:

Dearborn, MI - USA

Monthly Salary: Not provided by the employer

Posted: 23 June 2026 (30+ days ago)

Application Deadline: 20 September 2026

Vacancies: 1 Vacancy

Job Summary

Role: Sr Cloud Database Engineer/GCP Cloud Architect/Database platform Engineer

Location: Dearborn MI

Position Description:

5 years of experience to join our Platform Engineering team.
This role focuses on defining strategy architecture and standardized golden paths for cloud databases and the AI journey on Google Cloud Platform (GCP).
This position extends beyond traditional database administration. You will act as a technical leader and pattern owner shaping the vision and roadmap for our database platform.
A key focus is embedding AI/GenAI capabilities (e.g. Gemini Enterprise Model Context Protocol Dataplex and Knowledge Catalog) into how engineering teams design build secure and operate intelligent data systems at scale.

Key Responsibilities

1. AI-Driven Database Innovation GenAI Productivity: Lead adoption of Gemini Enterprise for tasks such as query generation debugging and schema optimization.

Contextual AI Patterns: Define patterns for integrating Model Context Protocol (MCP) with databases for contextual AI interactions.

2. Intelligent Automation: Identify and implement AI-driven automation across the lifecycle including capacity planning anomaly detection and self-tuning.

AI/ML Collaboration: Partner with AI/ML teams to embed ML statistical models and GenAI into data platform strategies.
Agent Development: Build intelligent agents on database AI frameworks to deliver business value improve fault detection and reduce OLTP costs.

3. Metadata Governance & Compliance Metadata & Discovery: Establish enterprise standards for metadata including tagging classification lineage and discoverability.

Data-as-a-Product: Promote well-documented discoverable and consumable datasets.
Security & Compliance: Align with frameworks such as GDPR and HIPAA; manage GCP IAM service accounts role bindings and encryption.
Implement secure agent practices (e.g. OBO authorization).

4. Database SRE & Performance Engineering SRE: Apply reliability practices to ensure availability fault tolerance and disaster recovery.

Performance Tuning: Lead query optimization indexing strategies and troubleshooting across distributed systems.
DevSecOps: Enable secure CI/CD pipelines for database code and configurations.

5. Database Architecture IaC & Platform Strategy Define Golden Paths: Establish scalable secure self-service database architectures and roadmaps on GCP.

Infrastructure as Code: Build and maintain Terraform templates for automated provisioning and lifecycle management.
Multi-Engine & Serverless Integration: Architect across SQL (SQL Server PostgreSQL MySQL AlloyDB Spanner) and NoSQL (MongoDB Firestore Bigtable Memorystore Neo4j) with Cloud Functions and Cloud Run.
Data Modeling & ETL: Develop complex data models and reliable ETL pipelines integrating multiple data sources.

6. Cross-Team Leadership & Enablement Thought Leadership: Advise engineering data and AI teams on best practices.

Culture & Mentorship: Foster an AI-assisted platform-first culture and mentor engineers.
Required Qualifications Experience: 5 years in database engineering with GCP AI enablement focus.
AI/GenAI Familiarity: Experience with Gemini Enterprise (or similar) and understanding of MCP or equivalent patterns.
Data Governance: Experience with Dataplex and Knowledge Catalog for metadata and lineage.
Multi-Engine Proficiency: Hands-on experience with GCP databases including Cloud SQL AlloyDB Spanner Firestore Memorystore MongoDB and BigQuery.
Security & DevSecOps: Knowledge of GCP IAM encryption and CI/CD practices.
Preferred Qualifications Platform Engineering: Experience building Internal Developer Platforms (IDPs).
Certifications: GCP Professional Cloud Database Engineer or Data Engineer. Automation & Coding: Proficiency in Terraform and programming (Python/Java).
Cloud-Native Ecosystems: Experience with Kubernetes (GKE) and database operators. Advanced Analytics: Understanding of statistical analysis data modeling and ML libraries.

Skills Required:

GCP

Skills Preferred:

Artificial Intelligence & Expert Systems

Additional Information:

Key Responsibilities

AI-Driven Database Innovation GenAI Productivity: Lead the adoption of Gemini Enterprise for database engineering productivity (e.g. automated query writing debugging and schema optimization).
Contextual AI Patterns: Define and implement patterns for integrating the Model Context Protocol (MCP) with databases to enable seamless contextual AI interactions.
Intelligent Automation: Identify and deploy AI-driven automation opportunities across the database lifecycle including predictive capacity planning anomaly detection and self-tuning.
AI/ML Collaboration: Partner with AI/ML teams to embed machine learning libraries statistical modeling and GenAI capabilities into broader data platform strategies.
Agent Development: Build agents on top of database AI framework that drive business value fault detection and cost reduction in the OLTP space at source and instant value realization.