Build the Foundation for Data-Driven Success
Transform fragmented data sources into a unified, scalable data infrastructure. Our data engineering solutions consolidate, transform, and manage enterprise data—delivering a single source of truth that powers intelligent decision-making across your organization.
What We Deliver
Comprehensive data engineering and warehousing services
ETL/ELT Pipeline Development
Design and build robust data pipelines that extract, transform, and load data from multiple sources. Automated, scheduled workflows with error handling and monitoring.
Cloud Data Warehousing
Implement modern cloud data warehouses on Snowflake, AWS Redshift, Google BigQuery, or Azure Synapse. Scalable, cost-efficient, and optimized for analytics.
Data Lake Architecture
Build centralized data lakes on AWS S3, Azure Data Lake, or Google Cloud Storage. Store structured, semi-structured, and unstructured data at scale.
Real-Time Data Streaming
Implement streaming data pipelines using Apache Kafka, AWS Kinesis, or Azure Event Hubs for real-time analytics and event-driven architectures.
Big Data Solutions
Process massive datasets with Apache Spark, Hadoop, or Databricks. Distributed computing for complex transformations and analytics at scale.
Data Quality & Governance
Implement data quality frameworks, validation rules, lineage tracking, and governance policies. Ensure trustworthy, compliant data across the enterprise.
Legacy Data Migration
Migrate from on-premise databases and legacy systems to modern cloud platforms. Zero downtime migrations with comprehensive testing and validation.
Data Orchestration
Automate complex workflows with Apache Airflow, Prefect, or cloud-native orchestration tools. Schedule, monitor, and manage data pipelines.
API & Integration Development
Build custom APIs and integrations to connect data sources, SaaS applications, and third-party systems to your data infrastructure.
Why Modern Data Infrastructure Matters
The business impact of world-class data engineering
Single Source of Truth
Eliminate data silos and inconsistencies. Consolidate data from CRM, ERP, marketing tools, databases, and APIs into one unified platform everyone can trust.
Faster Time to Insights
Reduce report generation from days to minutes. Real-time and near-real-time data availability enables instant decision-making and faster response to market changes.
Scalable Infrastructure
Cloud-native architecture that grows with your business. Handle 10x data volume growth without performance degradation or infrastructure rewrites.
Reduced Data Costs
Save 30-60% on data infrastructure costs vs. traditional on-premise warehouses. Pay only for what you use with cloud platforms' elastic pricing models.
Enterprise-Grade Security
Built-in encryption, role-based access control, audit logging, and compliance with GDPR, SOC 2, HIPAA, and industry regulations.
Automated Data Quality
Automated validation, cleansing, and monitoring ensure high-quality data. Catch errors early and maintain data integrity across all pipelines.
Improved Collaboration
Break down barriers between teams. Data engineers, analysts, and business users work from the same data platform with appropriate access controls.
Future-Proof Technology
Modern, cloud-native platforms that integrate seamlessly with AI/ML, streaming analytics, and emerging technologies. Built to evolve with your needs.
Our Data Engineering Process
Proven methodology for successful data infrastructure projects
Data Landscape Assessment
We audit your current data sources, systems, and workflows. Identify pain points, data quality issues, and business requirements. Document data flows and integration points.
Architecture Design & Planning
Design comprehensive data architecture including source connections, transformation logic, storage layers, and consumption patterns. Select optimal technologies and create detailed implementation roadmap.
Infrastructure Setup & Implementation
Provision cloud resources, set up data warehouse/lake, configure security and access controls. Build initial data pipelines and establish development workflows.
Data Migration & Integration
Migrate historical data from legacy systems. Build and deploy ETL/ELT pipelines for all data sources. Implement incremental loading strategies and error handling.
Testing & Optimization
Comprehensive testing of data pipelines, transformations, and quality checks. Performance optimization, cost optimization, and query tuning to ensure efficient operations.
Monitoring & Continuous Improvement
Deploy monitoring dashboards, alerting, and automated health checks. Ongoing optimization, new data source integration, and evolution of data architecture as needs grow.
Our Technology Stack
Best-in-class tools and platforms
Snowflake
Cloud data warehouse
Databricks
Unified data analytics
AWS Redshift
Amazon data warehouse
Google BigQuery
Serverless warehouse
dbt (data build tool)
Data transformation
Apache Spark
Big data processing
Apache Kafka
Event streaming
Apache Airflow
Workflow orchestration
Python
Data engineering
SQL
Query language
AWS S3
Data lake storage
Docker
Containerization
Data Engineering Pricing
Flexible packages to fit your needs and budget
Starter
£20K-£40K
- Cloud data warehouse setup (Snowflake/Redshift)
- 3-5 data source integrations
- Basic ETL pipeline development
- Data modeling & schema design
- Basic monitoring & alerting
- 2 months implementation timeline
- Knowledge transfer & documentation
Best For: Startups, SMBs with straightforward data needs, companies consolidating 3-5 systems
Professional
£40K-£100K
- Advanced data warehouse architecture
- 10-15 data source integrations
- Complex ETL/ELT pipelines with orchestration
- Data lake implementation
- Real-time streaming (optional)
- Data quality & governance framework
- Advanced monitoring & optimization
- 3-4 months implementation
- Training & ongoing support (3 months)
Best For: Mid-market companies, businesses with complex data needs, companies with 10-15+ data sources
Enterprise
£100K+
- Multi-environment architecture (dev/staging/prod)
- 20+ data source integrations
- Advanced big data solutions (Spark, Hadoop)
- Real-time streaming infrastructure
- Legacy system migration
- Enterprise data governance & compliance
- Multi-region/global deployment
- Disaster recovery & high availability
- 4-6 months implementation
- Dedicated support team & SLAs
Best For: Large enterprises, complex migrations, global organizations, regulated industries
Data Engineering Success Stories
Real results from our infrastructure projects
Real-Time Data Warehouse Drives £2.5M Revenue
UK fashion retailer migrated from legacy Oracle warehouse to Snowflake with real-time inventory sync from 200+ stores. Reduced stockouts by 45% and improved forecasting accuracy to 92%.
Data Lake Handles 50TB Daily with 99.9% Uptime
London SaaS company built AWS data lake with Spark processing for product analytics. Processes 50TB daily from 10M+ users with sub-second query performance.
What Our Clients Say
Rachel Kumar
Chief Data Officer, UK Retail Chain
Lisa Thompson
VP Engineering, SaaS Company
Frequently Asked Questions
How long does a data warehouse implementation take?
Timeline depends on complexity. A basic Snowflake warehouse with 3-5 sources takes 6-8 weeks. Mid-market implementations with 10-15 sources typically take 3-4 months. Enterprise projects with 20+ sources and complex requirements take 4-6 months. We deliver incrementally so you see value early.
Which cloud data warehouse is best?
Snowflake is our top recommendation for most use cases—best performance, easiest to use, and excellent cost-efficiency. AWS Redshift is ideal if you're heavily invested in AWS ecosystem. BigQuery works well for Google Cloud customers. We'll assess your needs and recommend the optimal platform.
Can you migrate from our legacy on-premise warehouse?
Absolutely. We've migrated clients from Oracle, SQL Server, Teradata, and other legacy platforms to modern cloud warehouses with zero downtime. We use proven migration patterns including parallel running, incremental cutover, and comprehensive testing.
What ongoing maintenance is required?
Cloud data warehouses require minimal maintenance vs. traditional systems—no hardware, no patches, no version upgrades. You'll need to monitor pipeline performance, optimize queries, add new data sources, and manage users. We offer managed services if you prefer us to handle this.
How do you ensure data quality?
We implement comprehensive data quality frameworks including: automated validation rules, schema enforcement, duplicate detection, null checks, referential integrity, data profiling, anomaly detection, and continuous monitoring. Quality issues trigger alerts for immediate remediation.
Ready to Build Your Data Infrastructure?
Get a free assessment and custom roadmap for your data engineering project.