Job Type

Consulting / Contract

Contract Structure

Rolling, iterative approach: design specific data domains, deploy them, validate outcomes, and refine as needed
Continue this design -> deploy -> validate cycle across different sections of the architecture until completion
Estimated duration: 4-6 months, depending on progress and collaboration

Job Overview

We are seeking an experienced Database Infrastructure & Data Architecture Consultant to design and implement company-level data architecture for a financial sales and services company covering insurance, tax, investment, and lending services.

The consultant will be responsible for developing a scalable, secure, and flexible database infrastructure that supports operational systems, analytics, and long-term data management.

The role requires expertise across multiple database technologies, including SQL and NoSQL systems, and experience designing multi-layer data architectures such as operational databases, data warehouses, data lakes, and analytical data marts. The consultant should have hands-on experience with major database platforms and cloud environments, including systems such as MongoDB, Azure, and Google Cloud, and should be able to recommend the appropriate architecture for both cloud-based and on-premise environments.

This role requires deep expertise across major database technologies, cloud platforms, data security, and enterprise data governance, and the ability to provide strategic guidance to executive leadership on the company's long-term data strategy.

Key Responsibilities

Enterprise Data Architecture Design

• Design and document a scalable company-wide database architecture that supports operational systems, reporting, analytics, and future business growth.
• Develop a multi-layer data architecture, which may include transactional databases, data warehouses, data lakes, and analytical data marts.
• Define data models, schema structures, and system relationships across different data environments.
• Support future business applications by designing data structures that go beyond basic document storage and retrieval, enabling efficient multi-level and multi-category data modeling to support analytics, AI, and intelligent business insights.
• Develop and manage a unified enterprise Document / Knowledge Repository to centrally organize business documents, policies, and data-related materials, with capabilities such as efficient search, access control, version management, and cross-system integration to improve information accessibility and collaboration.
• Provide architecture diagrams, documentation, and implementation guidelines.

Database Platform Evaluation & Selection

• Evaluate and recommend appropriate database technologies across both SQL and NoSQL platforms.
• Design database structures that leverage multiple well-established database platforms depending on use cases.
• Ensure the architecture supports flexibility, scalability, and long-term technology evolution.
• Multi-Platform & Hybrid Infrastructure Strategy
• Design database infrastructure that can operate across cloud environments and on-premise systems.
• Provide architecture recommendations for major cloud platforms such as Microsoft Azure, Google Cloud Platform (GCP), and other cloud database services.
• Design hybrid architectures that allow secure and efficient data operations across cloud and local systems.

Data Integration Framework

• Design the overall data integration framework connecting internal platforms, external systems, and partner integrations.
• Define strategies for data ingestion, data pipelines, synchronization, and data flow management across multiple systems.
• Ensure data consistency, reliability, and accessibility across the organization.

Database Security & Data Protection

• Design and implement database security architecture, including access control policies and role-based permissions.
• Develop architectural controls to minimize risks of data leakage, unauthorized access, and data breaches.
• Implement encryption, audit logging, monitoring, and security best practices for sensitive data.
• Ensure compliance with industry data protection standards and regulatory requirements.

Performance, Reliability & Data Governance

• Design systems to ensure high availability, scalability, backup, and disaster recovery capabilities.
• Define best practices for database performance optimization, indexing strategies, and workload management.
• Ensure the architecture supports future growth, advanced analytics, and AI-driven applications.
• Establish data governance standards, including data quality, ownership, monitoring, and lifecycle management.

Technical Leadership & Advisory

• Provide strategic guidance on long-term database infrastructure and data architecture development.
• Collaborate with engineering, product, and business teams to ensure the data architecture supports business operations and analytics needs.
• Provide technical recommendations for future system expansion and technology adoption.