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Add a senior data architect who can turn scattered requirements into an implementable target state for cloud platforms, data models, integration, governance, security, migration and operating ownership.
Hire a data architect when your team needs decisions that connect business domains, data models, platforms, integration, governance and delivery sequencing. This data architecture consulting scope can be delivered as a focused sprint, fractional role, embedded architect or defined project; scope it around the decisions and artifacts it must own — not a generic list of tools. Once decisions are approved, our data engineering services team can carry them into implementation.
Map sources, consumers, dependencies, ownership, constraints and failure points, then define a target state with explicit principles and trade-offs.
Create conceptual, logical and physical models, domain boundaries, canonical definitions and stewardship responsibilities that implementation teams can use.
Select and shape warehouse, lake, lakehouse or hybrid patterns across Snowflake, Databricks, BigQuery, Redshift, Synapse or Microsoft Fabric.
Define batch, CDC, API and event patterns with contracts, orchestration, replay, reconciliation, failure handling and measurable latency objectives.
Plan classification, catalog, lineage, access, masking, retention, quality ownership and audit evidence around the client's actual obligations.
Sequence migration waves, coexistence, validation, cutover and decommissioning while making performance, resilience and platform-cost decisions visible.
Our data architects bring hands-on experience across enterprise modeling, cloud platforms, warehouse and lakehouse design, governance and integration — the decisions that connect to reporting and analytics through our data analytics services team.
Target-state platform, domain and technology decisions that connect business capabilities to the systems that must implement them.
Domain boundaries, canonical definitions and stewardship responsibilities implementation teams can build against.
Platform decisions matched to identity, network, security and cost constraints across AWS, Azure and Google Cloud.
Warehouse, lake, lakehouse or hybrid pattern selection across Snowflake, Databricks, BigQuery, Redshift, Synapse or Microsoft Fabric.
Classification, catalog, lineage, access and retention design tied to the client's actual regulatory and quality obligations.
Batch, CDC, API and event architecture with contracts, orchestration, replay and measurable latency objectives.
Review a representative role profile, then ask us for two or three current CVs matched to your business domains, architecture scope, cloud, data platforms, governance obligations, implementation team and required working-hour overlap.
These projects are already published on DreamzTech's site and show architecture decisions implemented in production. Each card links to the full case study for complete metrics and context.
Industry: Transportation & Logistics
Core Technology: Snowflake, Power BI, SQL Server, ETL
Legacy SSRS reporting produced fragmented KPIs and slow report generation. DreamzTech redesigned the analytical platform around Snowflake with row-level security, automated ETL and Power BI dashboards, reducing report load times from 30 seconds to under 10 seconds while maintaining a 99% weekly data-health check pass rate.
Industry: Consumer Beverage
Core Technology: Unified analytics platform, historical decomposition modeling
The client needed consistent definitions and attribution across volume, net revenue, market share and ROI. DreamzTech designed a unified analytics structure with automated reporting, cutting manual reporting workflows by roughly 40% and reducing report generation time by approximately 60%.
Industry: Real Estate Data Aggregation
Core Technology: Public-record ingestion, canonical property model, AVM/CMA
DreamzTech designed a searchable data platform for deeds, liens, mortgages, tax assessments and permits covering over 90% of U.S. counties, plus an automated valuation engine. The platform generated 100,000+ property reports in its first six months, reached 12,000+ monthly active users and holds a 74% monthly retention rate.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call begins with the business domains, decisions, constraints and implementation team — not a preferred diagramming tool. Share what is unclear, what must change and which artifacts people need before they can build.
Share your data architecture requirements and we will design the fastest path to an implementable target state using proven patterns and our delivery team.
A capability map, not a promise that one architect knows every product — the approved profile is matched to your business domains, architecture scope, selected cloud, platform, modeling approach and governance obligations. Reporting and semantic-layer work is covered alongside our BI software development team, and if your workload centers on one specific platform, see our hire Databricks developers or hire Snowflake developers pages.
| Core Data Architecture Tools | ERwin Data ModelerSAP PowerDesignerSqlDBMdbdiagram.ioHackoladeLucidchartMicrosoft VisioSparx Enterprise Architect |
| Cloud Platforms | AWSMicrosoft AzureGoogle CloudSnowflakeDatabricksAmazon RedshiftGoogle BigQueryAzure SynapseMicrosoft Fabric |
| Data Integration | FivetranAirbyteKafka ConnectDebeziumMuleSoftBoomiREST/GraphQL APIs |
| ETL Tools | AWS GlueAzure Data FactoryData Factory in Microsoft FabricGoogle Cloud DataflowInformaticaMatillionTalenddbt |
| Programming Languages | SQLPythonScalaJavaBash |
| ML Frameworks | MLflowFeature-Store Patternsscikit-learnPyTorchTensorFlow |
| AI Tools | RAG Ingestion & Evaluation ArchitectureVector & Relational Serving LayersApproved LLM Provider Integrations |
| Streaming | Apache KafkaConfluentAmazon KinesisAzure Event HubsGoogle Pub/SubSpark Structured StreamingApache Flink |
| Visualization | Power BITableauLookerSigmaApache Superset |
| DevOps | TerraformDockerKubernetesGitHub ActionsGitLab CIAzure DevOpsArchitecture Decision Records |
| Version Control | GitHubGitLabBitbucketAzure Repos |
Hire a dedicated data architect for your project with a clear, efficient hiring process. Move from scattered requirements to an implementable plan faster.
Tell us your business domains, architecture scope, cloud and overlap needs so we can start matching the right data-architecture profiles.
Review matched data architect profiles and interview the ones that fit your architecture scope and working hours.
Confirm scope, contracting, security review and source access, then start onboarding on a realistic, confirmed date.
Hire a data architect who delivers scalable, high-performance data solutions across the industries we already serve.
A useful data architect does more than draw a target-state diagram. DreamzTech can combine architecture, data engineering, cloud, software, QA and delivery skills so recommendations remain connected to the systems, teams and operating constraints that must implement them.
Share the platform, model, governance, integration, migration or AI-data decision your team needs to make. We will respond with the likely architecture scope, relevant profiles, delivery questions and a practical next step.
Got questions about hiring a data architect? Explore direct answers below on role scope, deliverables, cloud fit and cost.
A data architect turns business requirements, data domains and technical constraints into an implementable design for how data is collected, modeled, integrated, stored, governed, secured and served — including the AI-ready data layers our AI software development team builds on top of. The role should also document trade-offs, standards, migration sequence and ownership so engineers can build and operate the design.
A data architect defines the target structure, principles, models, integration patterns, governance and major technology decisions. A data engineer implements and operates pipelines, transformations, storage and platform components — see our hire data engineers page once architecture decisions are approved and you need engineering capacity. In smaller programs one senior person may cover parts of both roles, but ownership should be explicit.
Hire a data architect before a major warehouse or lakehouse build, cloud migration, domain redesign, data-governance program, merger integration or AI-data initiative when several teams need one coherent direction. An architect is less useful when the business outcome is still undefined or no delivery team or owner is available to implement the decisions.
Typical deliverables include a current-state assessment, architecture principles, target-state diagrams, conceptual and logical data models, domain boundaries, integration patterns, security and governance controls, technology decisions, migration waves, risks and an implementation handoff. The exact set should be agreed before the engagement so the work does not become diagram production without decision ownership.
Yes, provided the matched architect has relevant experience with your selected cloud, data platforms and governance model. The architect should design within your identity, network, security, cost and deployment constraints unless there is evidence that a phased change is necessary.
Cost depends on seniority, platform depth, domain complexity, regulatory obligations, working-hour overlap and whether you need an audit, fractional guidance, a full-time architect or an architecture-plus-delivery team. DreamzTech’s published starting rate is $20 per hour or $3,200 for a 160-hour monthly allocation once sales confirms the role; fixed scopes require discovery.
Profile matching can begin once the decision scope, current platforms, stakeholders, timeline and working-hour overlap are clear. The actual start date depends on architect availability, interviews, contracting and security access; prepare existing diagrams, system inventories, policies, pain points and known deadlines so the first sessions produce decisions rather than repeated discovery.