Hire Microsoft data engineers
Hire Microsoft Fabric Engineers for Modern Data & Analytics
Build a modern data platform with engineers who know Fabric, Power BI and the wider Microsoft data stack.
Turn fragmented business data into a reliable analytics foundation with Microsoft Fabric engineering expertise. Build data pipelines, lakehouse and warehouse solutions, and analytics workflows designed around your business requirements.
Fabric engineering skills
- Data Factory pipelines
- Notebooks & Spark
- Lakehouse & OneLake
- Fabric Data Warehouse
- Power BI semantic models
- Governance & access
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 Microsoft Fabric Engineer Do?
Microsoft Fabric is Microsoft’s software-as-a-service analytics platform. It brings data integration, data engineering, data warehousing, real-time analytics, data science and Power BI together over a shared data lake called OneLake.
A Fabric engineer designs and builds the platform on top of it: connecting sources, building pipelines and transformations, modelling lakehouses and warehouses, preparing semantic models for Power BI, and putting governance and monitoring in place. Fabric is not the right fit for every organisation; the choice depends on your data volumes, workloads, existing investments, requirements and cost. We help you make that decision honestly. We do not claim a Microsoft partner designation.
When to hire Microsoft Fabric engineers
- You are adopting Fabric and need a solid, well-governed foundation.
- Reporting relies on fragile spreadsheets or many disconnected pipelines.
- You want to consolidate Power BI, data engineering and warehousing.
- An existing warehouse or analytics platform needs modernizing.
Data Challenges Fabric Engineering Can Help Solve
Well-designed data platforms make reporting more reliable and easier to change. The right architecture depends on your situation.
Data scattered across systems
Bring data from ERP, CRM, files and APIs into one governed place.
Complex integration and transformation
Build maintainable pipelines instead of one-off scripts.
Slow or unreliable reporting
Replace fragile refresh chains with monitored, scheduled pipelines.
Silos between departments
Share curated data across teams without copying it everywhere.
Data quality and governance
Add validation, ownership and access rules.
Inconsistent business reporting
Agree shared definitions in reusable semantic models.
Legacy analytics platforms
Modernize older warehouses and reporting tools in stages.
Growing analytics needs
Scale workloads as data volumes and user numbers grow.
Microsoft Fabric Engineering Services
Practical services with clear technical deliverables.
Implementation and platform setup
Set up workspaces, capacities, environments and naming standards.
Deliverables: platform design, workspace structure
Data ingestion and pipelines
Connect sources and load data reliably.
Deliverables: pipelines, schedules, monitoring
Lakehouse engineering
Design and build lakehouses on OneLake.
Deliverables: lakehouse layers, Delta tables
Data warehouse development
Build SQL-based analytical warehouses.
Deliverables: warehouse schema, loading processes
Data transformation
Transform data with notebooks, SQL and dataflows.
Deliverables: tested transformation logic
Power BI semantic models
Model data for consistent, fast reporting.
Deliverables: semantic models, reports, measures
Real-time analytics
Stream and analyse event data where it adds value.
Deliverables: eventstreams, real-time dashboards
Migration and modernization
Move suitable workloads from legacy platforms.
Deliverables: migration plan, validated data
Governance, security and access
Control who can see and change what.
Deliverables: access model, sensitivity labels, workspace roles
Performance and ongoing support
Tune, monitor and improve the platform over time.
Deliverables: performance fixes, support runbooks
Microsoft Fabric Capabilities We Work With
Organised by area. Feature availability depends on your Fabric capacity, licensing and region, so we confirm what applies to you.
Data engineering
- Data Factory pipelines
- Dataflow Gen2
- Notebooks and Apache Spark
- Orchestration and scheduling
Lakehouse and warehouse
- OneLake
- Fabric lakehouse
- Fabric Data Warehouse
- SQL analytics endpoint
Analytics and BI
- Power BI integration
- Semantic models
- Reports and dashboards
Real-time and governance
- Real-Time Intelligence
- Workspace roles and permissions
- OneLake catalog
- Data quality checks
Microsoft Ecosystem Integration
Fabric’s native capabilities, such as OneLake, Data Factory in Fabric, lakehouses, warehouses and Power BI, cover much of the platform. Around them, we integrate with external services you may already use:
- Azure Data Factory and Azure Data Lake Storage (external Azure services).
- Azure SQL and Microsoft SQL Server databases.
- Microsoft Power Platform and SharePoint.
- REST APIs, files and third-party SaaS data sources.
- Azure services for security, networking and monitoring.
Hiring and Engagement Models
Dedicated Microsoft Fabric engineers
engineers working only on your data platform.
Data engineering teams
a lead engineer with developers and BI specialists.
Fabric implementation consulting
design advice and reviews for your own team.
Staff augmentation
add Fabric skills to your existing team.
Project-based data platform development
defined deliverables against agreed milestones.
Ongoing maintenance and optimization
support, monitoring and improvements after go-live.
Engage engineers for a defined deliverable or for longer-term development. Pricing depends on the role and duration, so request a quote.
Illustrative Use Cases
Examples of where Fabric engineers can help. These are illustrative, not descriptions of completed client projects.
Consolidating enterprise data
Bring finance, sales and operations data together for reporting.
Modern analytical platform
Build a layered lakehouse and warehouse for analytics.
Replacing fragmented pipelines
Swap scattered scripts and manual refreshes for monitored pipelines.
Sales and operations dashboards
Power BI reports on trusted, shared semantic models.
Governed self-service data
Give business teams access to curated data with the right permissions.
Migrating analytics workloads
Move suitable workloads from older warehouses or tools.
Data ready for AI and ML
Prepare clean, documented data for AI and ML development.
Near-real-time monitoring
Track operational events as they happen where the use case justifies it.
Our Fabric Delivery Methodology
A clear lifecycle from assessment to optimization.
Data landscape assessment
sources, volumes, users and current reports.
Requirements and architecture
target design and agreed scope.
Connectivity and ingestion
connect sources and land raw data.
Transformation and modelling
clean, join and model the data.
Analytics and reporting
semantic models and Power BI reports.
Quality, security and validation
tests, reconciliation and access checks.
Deploy, monitor and optimize
release, monitor and tune.
Data validated against source systems · Access granted on least privilege · Documented pipelines and models
Data Security and Governance
Good governance makes data trustworthy and safe to share.
- Workspace roles and item permissions designed around your teams.
- Row-level and object-level security in semantic models where needed.
- Sensitivity labels and Microsoft Purview integration where licensed.
- Data quality checks and reconciliation with source systems.
- Documented lineage, ownership and definitions.
- Secure handling of credentials and connections.
Why Prominent AI Services?
We deliver data engineering, Power BI and AI services across the Microsoft ecosystem.
- Experience with data engineering, Power BI and Azure.
- Data, BI and AI skills in one team.
- You interview and approve every engineer.
- Flexible full-time, part-time or hourly engagement.
- Explore our data and business intelligence services and the technologies we use.
Frequently Asked Questions
What does a Microsoft Fabric engineer do?
A Fabric engineer builds data platforms in Microsoft Fabric: pipelines, lakehouses, warehouses, transformations, semantic models and the governance around them.
When should a business hire Microsoft Fabric engineers?
When adopting Fabric, consolidating data and reporting, modernizing a legacy analytics platform, or when in-house teams need extra Fabric skills.
What is Microsoft Fabric used for?
It is used for end-to-end analytics: data ingestion, transformation, warehousing, real-time analysis, data science and Power BI reporting on shared OneLake storage.
What is the difference between Microsoft Fabric and Azure Data Factory?
Azure Data Factory is a standalone Azure data integration service. Fabric is a broader SaaS analytics platform that includes its own Data Factory experience alongside storage, warehousing and Power BI.
Can Microsoft Fabric integrate with Power BI?
Yes. Power BI is part of Fabric, and semantic models can use lakehouse and warehouse data directly.
Can you migrate an existing data warehouse to Microsoft Fabric?
Often, yes. We first assess whether Fabric suits the workload, then plan, migrate and validate the data in stages.
Can you build lakehouse and warehouse solutions?
Yes. We design and build both and help you decide which fits each workload.
How do you address data security and governance?
Through workspace roles, item permissions, row-level security, sensitivity labels where licensed, quality checks and documentation.
Can you provide dedicated Fabric engineers?
Yes. You can hire dedicated engineers full-time, part-time or hourly, and interview them first.
What engagement models are available?
Dedicated engineers, data engineering teams, implementation consulting, staff augmentation, project-based development and ongoing support.
Ready to Modernize Your Data Platform?
Tell us about your data sources, reporting needs, and analytics goals. Our team will help you identify a suitable Microsoft Fabric engineering approach.