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Join a team of Microsoft specialists building the future of data and AI.
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The Foundation of Trust
The overnight load failed again, the fix lives in one engineer's head, and finance has quietly gone back to Excel. We build data pipelines that are tested, observable and cheap to run, so the numbers hold when the board pack goes out.
Pipelines · Lakehouse · DataOps
A number changed overnight and nobody can say why. The morning refresh failed again, and the report was already in inboxes when someone noticed. Those are pipeline problems, and this page is about the discipline that fixes them: data engineering keeps the data underneath every dashboard, forecast and AI feature accurate, observable and cheap to run. It is unglamorous work, which is exactly why doing it well is a competitive advantage.
Our builds follow the medallion pattern on Microsoft Fabric or Azure Databricks: raw data lands in bronze, gets cleaned and conformed in silver, and is served as governed gold datasets with data contracts between the layers. Where the workload calls for streaming, we use Fabric Real-Time Intelligence or Spark Structured Streaming, credentials we hold as a Real-Time Intelligence Featured Partner.
We treat pipelines as software. Everything lives in source control, deploys through CI/CD, carries automated quality tests, and reports its own health through observability tooling. When something breaks at source, the alert fires before the business opens the report, not after.
A large share of our engineering work is modernisation rather than greenfield: retiring SSIS packages, hand-coded stored procedures and ageing ADF pipelines in favour of metadata-driven ELT, at whatever pace the estate can absorb. Mount Anvil's Fabric lakehouse migration cut report delivery times by 40% within three months.
Engineered on Microsoft Fabric, Azure and Databricks
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.
We implement data observability, DataOps practices, and predictive maintenance capabilities that give you full visibility into pipeline health and enable proactive issue resolution.
We build automated data quality testing, pipeline monitoring, and performance optimisation frameworks that ensure your data pipelines deliver accurate results on time, every time.
We design cloud data architectures and ETL/ELT pipelines that make full use of Azure, Microsoft Fabric, and Databricks for scalable, cost-effective data processing.
We bring deep expertise in data engineering development, tools, and technologies to deliver pipelines that are maintainable, extensible, and built on industry best practices.
Observability across ingest, transform and serve
Faster report delivery at Mount Anvil within three months
Fabric Featured designations, including Real-Time Intelligence
Every pipeline source-controlled, tested and deployed automatically
Design and deliver a medallion-style lakehouse on Fabric or Databricks so analytics, BI and AI teams draw from one governed, high-quality source.
Ingest IoT, transactional and clickstream events into KQL, Event Streams or Delta Live Tables for sub-minute operational analytics.
Retire SSIS, Informatica or hand-coded pipelines in favour of metadata-driven, source-controlled ELT in Fabric or Azure Data Factory.
Curate cleansed, well-described gold datasets that Copilot, agents and ML models can consume without bespoke glue code.
Introduce automated testing, lineage and SLAs so data incidents are detected and fixed before they reach the business.
Engineer auditable pipelines for finance, risk and compliance reporting with immutable history and full lineage back to source.
Profile sources, understand consumption patterns and agree non-functional requirements around latency, cost and quality.
Produce a reference architecture, data contracts and naming / layering standards aligned to Microsoft best practice.
Engineer pipelines with infrastructure-as-code, unit and integration tests, and CI/CD so every change is safe and traceable.
Stand up observability, alerting and DataOps rituals, then transition to your team or a Synapx-as-a-Service support model.
Lakehouse migrations and analytics engineering with the client's numbers attached.
A Power BI reporting suite connected to Jira Service Management, providing real-time visibility into ticket volumes, SLA compliance, and team performance.
A modern Microsoft Fabric data platform with master data management, daily snapshotting and Activator alerting, built as a foundation for AI at Mount Anvil.
A Power BI revenue reporting solution replacing manual Excel reports, delivering automated daily insights into revenue performance, technician productivity, and service trends.
"Not an external supplier, an extension of our team."
"Working with Synapx has been transformative for Mount Anvil. They didn't feel like an external supplier; they became an extension of our team. Their technical expertise in implementing Microsoft Fabric solved our immediate challenges and positioned us for future growth, including AI and machine learning. What truly set Synapx apart was their transparency, commitment, and cost-optimisation through Xscale, making Fabric financially sustainable."
Head of Technology Applications, Mount Anvil
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.
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.
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.
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.
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.
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.
Show us the pipeline that keeps you up at night. Within a week we will map failure points, missing tests and run costs across your estate, and hand you a fix list ordered by risk.
Book a Pipeline Health Review