Data Engineering & Cloud
Build a data foundation that can grow with your business.
We design and build modern data platforms that bring information together, automate data movement and transformation, and create a reliable foundation for analytics, applications and business operations.
What we can build
Engineering the foundation behind your data.
From individual pipelines to enterprise data platforms, we build the infrastructure and engineering processes needed to move, transform, organize and manage business data reliably.
Data Pipelines
Build reliable ETL and ELT pipelines for ingesting, transforming and delivering business data.
Microsoft Fabric
Design modern data platforms using Lakehouse, Warehouse, pipelines and data engineering workloads.
Azure Data Platforms
Build cloud data solutions for integration, storage, processing and analytics across Azure services.
Databricks & Spark
Develop scalable transformation and processing workloads using Databricks, PySpark and Spark SQL.
Lakehouse & Warehouse
Organize business data into governed analytical layers designed for performance, consistency and reuse.
Data Migration & Modernization
Move legacy databases, pipelines and analytics workloads onto modern cloud data platforms.
Data Quality & Governance
Build validation, lineage, access controls and quality processes into the data platform.
System & API Integration
Connect applications, databases, files, APIs and external platforms through unified data workflows.
Example Architecture
Enterprise Data Platform
A modern data platform can connect operational systems, automate ingestion and transformation, organize information into trusted layers and make business-ready data available to analytics, applications and other data products.
Modern Data Platform
Example enterprise architecture
Sources
Business Systems
Operational systems where business data originates.
Integration
Ingestion & Data Movement
Reliable processes for bringing information into the platform.
Raw Data
Source-aligned information captured with minimal transformation.
Validated & Standardized
Cleaned, validated and standardized information ready for reuse.
Business-Ready Data
Curated business entities, metrics and analytical datasets.
Analytics
Reports & BI
Dashboards, KPIs and analytical reporting.
Applications
Apps & APIs
Business applications and operational data services.
Data Products
Sharing & AI
Reusable datasets, machine learning and downstream consumption.
Architecture is adapted to each organization's systems, security requirements, data volumes, operating model and business objectives.
Data modernization
From legacy systems to a modern data platform.
Modernization is more than moving data to the cloud. We look at architecture, pipelines, transformations, quality, governance and how the platform will operate after migration.
01
Assess
Understand existing systems, data sources, workloads, dependencies and business requirements.
02
Design
Define the target architecture, data layers, integration patterns and operating model.
03
Migrate
Move data and workloads incrementally while reducing disruption to existing business processes.
04
Engineer
Build automated pipelines, transformations, reusable data models and platform components.
05
Govern
Introduce quality controls, lineage, security, monitoring and trusted data standards.
06
Operate
Support reliable production workloads with monitoring, optimization and controlled deployments.
Built for production
Reliable data engineering beyond the first deployment.
A data platform needs to remain reliable as systems, teams and business requirements change. We design engineering practices around maintainability, controlled deployments, monitoring and data quality from the beginning.
Automated Pipelines
Repeatable ingestion and transformation processes designed to reduce manual intervention.
Environment Strategy
Structured development, testing and production environments with controlled releases.
Data Quality
Validation and monitoring designed to identify issues before they reach consumers.
Security & Governance
Access, lineage and governance practices incorporated into the platform architecture.
Observability
Monitoring of pipeline execution, failures, dependencies and platform health.
Scalable Architecture
Solutions designed to evolve as data volumes, sources and business requirements grow.
Business use cases
Data platforms built around real business needs.
Whether the goal is replacing legacy infrastructure, supporting analytics or connecting disconnected systems, the architecture should solve a clear business problem.
Cloud Modernization
Modernize legacy data infrastructure using scalable cloud-native architecture.
Analytics Foundations
Create trusted data layers that support reporting, analytics and business intelligence.
Legacy Migration
Move existing databases, ETL processes and analytical workloads onto modern platforms.
Data Integration
Connect information across operational systems, applications, databases, files and APIs.
System Consolidation
Bring fragmented business data together into a consistent and reusable data foundation.
Enterprise Data Platforms
Design governed platforms capable of supporting multiple teams, workloads and data products.
Planning a data platform or modernization?