The Foundation of Trust
Precision Data Engineering
Our data engineering services ensure pipelines are accurate, secure, and timely. We deliver modern data pipelines built for high-volume batch processing, real-time streaming, and ML-ready workloads.
Engineered on Microsoft Fabric, Azure and Databricks
Microsoft Fabric Featured Partner 
Data & AI on Azure 
Real-Time Intelligence Featured Partner 
Fabric Databases Featured Partner Key Capabilities
Data Ingestion, Collection & Transformation
We design and implement robust data ingestion and collection processes, with transformation and storage solutions that ensure your data is clean, consistent, and ready for analysis.
Data Observability & DataOps
We implement data observability, DataOps practices, and predictive maintenance capabilities that give you full visibility into pipeline health and enable proactive issue resolution.
Data Quality Testing & Pipeline Monitoring
We build automated data quality testing, pipeline monitoring, and performance optimisation frameworks that ensure your data pipelines deliver accurate results on time, every time.
Cloud Data Architecture
We design cloud data architectures and ETL/ELT pipelines that leverage the full power of Azure, Microsoft Fabric, and Databricks for scalable, cost-effective data processing.
Data Engineering Tools & Technologies
We bring deep expertise in data engineering development, tools, and technologies to deliver pipelines that are maintainable, extensible, and built on industry best practices.
Pipeline reliability SLA on managed estates
Reduction in manual data preparation effort
Faster time-to-production for new data domains
Observability across ingest, transform and serve
Where Precision Data Engineering drives value
Modern lakehouse build
Design and deliver a medallion-style lakehouse on Fabric or Databricks so analytics, BI and AI teams draw from one governed, high-quality source.
Real-time event streaming
Ingest IoT, transactional and clickstream events into KQL, Event Streams or Delta Live Tables for sub-minute operational analytics.
Legacy ETL modernisation
Retire SSIS, Informatica or hand-coded pipelines in favour of metadata-driven, source-controlled ELT in Fabric or Azure Data Factory.
AI-ready data domains
Curate cleansed, well-described gold datasets that Copilot, agents and ML models can consume without bespoke glue code.
Data quality and observability
Introduce automated testing, lineage and SLAs so data incidents are detected and fixed before they reach the business.
Regulatory data supply
Engineer auditable pipelines for finance, risk and compliance reporting with immutable history and full lineage back to source.
A proven delivery approach
- 01 Step
Discover
Profile sources, understand consumption patterns and agree non-functional requirements around latency, cost and quality.
- 02 Step
Design
Produce a reference architecture, data contracts and naming / layering standards aligned to Microsoft best practice.
- 03 Step
Build
Engineer pipelines with infrastructure-as-code, unit and integration tests, and CI/CD so every change is safe and traceable.
- 04 Step
Operate
Stand up observability, alerting and DataOps rituals, then transition to your team or a Synapx-as-a-Service support model.
Frequently asked questions
Fabric or Databricks — which should we use?
Both are excellent and we deliver on both. Fabric tends to win when Power BI is central, when you want a SaaS experience with consolidated licensing, and when real-time intelligence matters. Databricks tends to win for heavy ML, very large Spark workloads and multi-cloud estates. Many clients run both.
How long does a data engineering engagement take?
A first domain — e.g. finance, sales or operations — typically takes 8–14 weeks from discovery to production, including governance and CI/CD. Subsequent domains are faster as the platform and patterns are reused.
Do you follow DataOps and software engineering practices?
Yes. Every pipeline we build is in source control, code-reviewed, automatically tested and deployed through CI/CD. We use Git-integrated Fabric, Azure DevOps / GitHub Actions, and treat data products with the same rigour as application code.
Can you modernise our existing SSIS / ADF pipelines?
Yes. We run lift-shift-optimise programmes that move legacy ETL into Fabric Data Factory or Azure Data Factory, re-platforming only where it delivers measurable value, so you avoid an expensive big-bang rewrite.
How do you handle data quality and reliability?
We implement data contracts at source, automated tests in pipelines (Great Expectations, dbt tests, Fabric data quality), and observability via Purview and Monitor. SLAs, runbooks and on-call rotations make reliability operational, not aspirational.
Can you run the platform for us after go-live?
Yes. Synapx-as-a-Service provides ongoing platform operations, cost optimisation, enhancement and on-call support, with UK-based engineers who already know your estate.
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