Database Architect

We are seeking an experienced Database Architect with strong database knowledge and hands-on cloud experience to design, optimize, and modernize scalable data platforms. The ideal candidate will have expertise in relational and NoSQL databases, cloud-based database services, data architecture, and performance optimization, along with working knowledge of Python and AI/ML technologies.

This role will partner with application, data engineering, cloud, security, and AI teams to define database architecture standards and build reliable, secure, and scalable data solutions.

Key Responsibilities

• Design and define enterprise-level database architectures, standards, patterns, and best practices.
• Evaluate and select appropriate relational, NoSQL, and cloud-native database technologies based on business and technical requirements.
• Design scalable database solutions covering data modeling, availability, resiliency, backup/recovery, replication, and disaster recovery.
• Provide expertise in database technologies such as PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, or similar platforms.
• Design and support database solutions on cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform).
• Lead or support on-premises-to-cloud database migrations and modernization initiatives.
• Optimize database performance through query tuning, indexing, partitioning, capacity planning, and database configuration.
• Establish database security standards, including encryption, access controls, auditing, data protection, and compliance requirements.
• Collaborate with application architects and engineering teams on database design, APIs, data access patterns, and integration solutions.
• Use Python for basic database automation, scripting, data processing, validation, and operational tasks.
• Work with AI/ML and GenAI teams to understand data requirements and support AI-enabled applications.
• Provide basic architectural guidance for AI-related data patterns, including embeddings, vector databases/vector search, and Retrieval-Augmented Generation (RAG).
• Evaluate emerging database, cloud, and AI technologies and recommend solutions where appropriate.
• Create and maintain architecture diagrams, database standards, technical documentation, and design decisions.
• Provide technical leadership, architecture reviews, troubleshooting support, and mentoring to engineering teams.

Required Qualifications

• Strong experience in database architecture, database engineering, or database administration.
• Deep understanding of relational database concepts, SQL, data modeling, normalization, indexing, transactions, and performance optimization.
• Experience with one or more major database platforms such as PostgreSQL, Oracle, SQL Server, MySQL, MongoDB, or equivalent.
• Hands-on experience with AWS, Azure, or Google Cloud Platform, particularly managed database services.
• Understanding of cloud architecture concepts including scalability, high availability, security, monitoring, backup, and disaster recovery.
• Experience designing or supporting database migration and modernization initiatives.
• Basic to intermediate Python skills for scripting, automation, and data processing.
• Basic understanding of Artificial Intelligence, Machine Learning, and Generative AI concepts.
• Familiarity with AI-related technologies such as vector databases, embeddings, semantic search, LLMs, and RAG is preferred.
• Strong problem-solving, analytical, communication, and documentation skills.
• Ability to collaborate effectively with architects, developers, data engineers, infrastructure teams, security teams, and business stakeholders.

Preferred Qualifications

• Experience with cloud-native and distributed database architectures.
• Experience with Infrastructure as Code and DevOps/CI/CD practices.
• Knowledge of database observability, monitoring, and automated operations.
• Experience with data governance, security, privacy, and regulatory requirements.
• Exposure to AI/ML platforms and cloud-based AI services.
• Experience supporting large-scale, high-volume, highly available enterprise systems