Hire enterprise data architects
Hire Data Architects to Design Scalable Enterprise Data Platforms
Build a strong foundation for your data, analytics and AI initiatives.
Design secure, scalable, and reliable data platforms with architecture expertise that connects business requirements, enterprise systems, analytics, and AI initiatives.
Data architecture expertise
- Current & target-state design
- Warehouse & lakehouse
- Data modelling
- Integration patterns
- Governance & security
- Analytics & AI readiness
Profiles in 48–72 hours · You interview every specialist
- Technically vetted specialists
- Profiles in 48–72 hours
- Full-time, part-time or hourly
What Does a Data Architect Do?
A data architect designs how data is collected, stored, integrated, modelled, governed and served across an organisation. They turn business requirements into a target architecture, choose technologies and patterns, set standards, and guide engineers as the platform is built.
Good data architecture is about maintainability, reliability, governance and business value, not just tools. Our data architects work with your business stakeholders, data engineers, BI developers, security teams and application architects so the design works in practice.
When to hire a data architect
- Data is spread across systems with no shared definitions.
- You are planning a new data platform, warehouse or lakehouse.
- Reporting is unreliable or hard to change.
- You need governance and security in place before scaling analytics or AI.
Business Challenges Data Architecture Addresses
Architecture decisions made early prevent costly rework later.
Fragmented enterprise data
Define where data lives and how it flows between systems.
Inconsistent definitions
Agree shared business terms and models.
Poor data quality
Design validation and ownership into the platform.
Complex integrations
Choose integration patterns that are reliable and maintainable.
Unreliable reporting
Build a trustworthy foundation for BI.
Scalability and performance
Plan for growth in data volumes, users and workloads.
Security and governance
Classify data and control access from the start.
Legacy warehouse modernization
Modernize in stages without disrupting reporting.
Analytics and AI demands
Prepare governed, well-documented data for new use cases.
Data Architecture Services
Each service has a clear purpose and practical deliverables.
Enterprise architecture assessment
Understand your current data landscape and its gaps.
Deliverables: assessment report, risk list
Data platform strategy and roadmap
Plan where the platform needs to go and in what order.
Deliverables: target state, phased roadmap
Cloud data architecture
Design data platforms on Azure, Microsoft Fabric or AWS.
Deliverables: reference architecture, service choices
Warehouse and lakehouse architecture
Choose and design the right storage and serving layers.
Deliverables: layer design, storage standards
Data modelling and schema design
Model data for analytics and operational needs.
Deliverables: conceptual, logical and physical models
Integration and pipeline architecture
Define how data moves between systems.
Deliverables: integration patterns, pipeline standards
Governance and quality architecture
Build ownership, rules and checks into the design.
Deliverables: governance model, quality rules
Master data and metadata
Design catalogues and reference data where your platform supports it.
Deliverables: metadata approach, master data design
Security and privacy by design
Classify data and design access controls.
Deliverables: classification scheme, access model
Architecture modernization
Move from legacy warehouses to modern platforms.
Deliverables: migration architecture, transition plan
Analytics and AI data readiness
Prepare data foundations for BI and machine learning.
Deliverables: readiness assessment, data preparation plan
Reviews and technical advisory
Independent reviews of designs and decisions.
Deliverables: review findings, recommendations
Data Architecture Expertise
How our architects organise their work.
Architecture and strategy
- Current-state assessment
- Target-state architecture
- Standards and roadmaps
- Technology selection and trade-offs
Engineering and modelling
- Data modelling
- Ingestion and integration patterns
- Transformation and quality
- Warehouse and lakehouse design
Governance and security
- Ownership and stewardship
- Classification and access control
- Lineage and metadata
- Governance and compliance requirements
Analytics and AI enablement
- Analytics platform foundations
- BI semantic model alignment
- Data preparation for AI and ML
- Reliable, governed data access
Technology Ecosystem
Our architects work with platforms our teams use in delivery. Not every tool is used in every engagement; decisions depend on requirements, data volumes, security, integration needs, skills, licensing and operating costs.
- Microsoft Azure and Microsoft Fabric.
- Azure SQL, SQL Server and Azure Data Lake Storage.
- Power BI and Microsoft Power Platform.
- AWS data services, PostgreSQL and MySQL.
- Python, data integration tools and APIs.
Hire Data Architects: Engagement Models
Dedicated data architects
an architect working only on your data programme.
Enterprise data architecture consulting
advice on strategy, standards and key decisions.
Cloud data architecture specialists
focused expertise for Azure, Fabric or AWS platforms.
Data platform design teams
an architect with data engineers and BI developers.
Architecture assessment engagements
a short, fixed-scope review with a clear report.
Project-based implementation support
architecture guidance while your platform is built.
Ongoing architecture advisory
regular reviews as your data estate grows.
Engage specialists for a short assessment or a longer data initiative. Pricing depends on scope and duration, so request a quote.
Illustrative Industry Applications
These are illustrative scenarios, not descriptions of completed client engagements or compliance certifications.
Healthcare
Unified reporting across clinical and operational systems with strict access controls (see healthcare).
Financial services
Governed data foundations for risk, finance and regulatory reporting (see financial services).
Manufacturing
Operational analytics combining production, quality and supply data (see manufacturing).
Retail and e-commerce
A single view of sales, stock and customers (see retail and e-commerce).
Logistics
Integrating shipment, fleet and partner data for timely reporting (see logistics).
Professional services
Project, time and finance data brought together for profitability reporting.
Data Architecture Delivery Methodology
Collaborative, documented and focused on decisions that last.
Business discovery
goals, stakeholders and requirements.
Current-state assessment
sources, flows, quality and pain points.
Target architecture
options, trade-offs and technology choices.
Modelling and integration design
data models and integration patterns.
Security, governance and quality
access, ownership and quality rules.
Implementation guidance
support engineers and validate the build.
Documentation and improvement
standards, records and ongoing review.
Works with business stakeholders, data engineers, BI developers, security teams and application architects
Governance and Security Approach
Governance is designed into the architecture, not added afterwards.
- Clear data ownership and stewardship roles.
- Data classification and least-privilege access.
- Lineage and metadata so data can be traced and trusted.
- Quality rules and monitoring at key points.
- Privacy considerations built into the design.
- Documented decisions and standards.
Why Prominent AI Services?
We deliver data engineering, BI, cloud and AI services, so our architecture advice is grounded in delivery.
- Hands-on experience with Azure, Microsoft Fabric, Power BI and SQL.
- Architects who work alongside our data and BI teams.
- Experience preparing data for AI and ML projects.
- You interview and approve every specialist.
Frequently Asked Questions
What does a data architect do?
A data architect designs how data is stored, integrated, modelled, governed and used across an organisation, and guides engineers who build the platform.
When should a company hire a data architect?
When planning a new data platform, modernizing a warehouse, fixing unreliable reporting, or preparing data for analytics and AI at scale.
What is the difference between a data architect and a data engineer?
A data architect designs the overall structure, standards and patterns. A data engineer builds and runs the pipelines, storage and transformations within that design.
What is the difference between a data architect and a solution architect?
A solution architect designs a complete system or application. A data architect focuses on the data across systems: models, integration, governance and analytics.
Can a data architect help modernize a legacy data warehouse?
Yes. They assess the current warehouse, design the target platform and plan a staged migration that keeps reporting running.
Can you design Microsoft Fabric and Azure data platforms?
Yes. We design data platforms on Microsoft Fabric and Azure, and can also work with AWS where needed.
How do you approach data governance and security?
Through ownership, classification, least-privilege access, lineage, quality rules and documented standards.
Can you support enterprise data integration?
Yes. We design integration patterns and pipelines that connect ERP, CRM, files, APIs and other sources.
Can a data architect help prepare data for AI and analytics?
Yes. We design clean, governed, well-documented data foundations that analytics and machine learning can rely on.
What engagement models are available?
Dedicated architects, consulting, cloud data specialists, platform design teams, assessments, implementation support and ongoing advisory.
Planning Your Next Data Platform?
Share your data challenges, current technology stack, and business goals. Let’s discuss an architecture approach that supports your organization’s next stage of growth.