Data Engineering & Integration
Connect your systems into one source of truth.
We build the pipelines and data model that bring your CRM, ERP, finance and operational data together, so reports and AI work from the same reliable numbers.
Problems this solves
- Different reports show different numbers for the same thing
- Data is copied between systems by hand
- Reporting depends on one person’s spreadsheets
- New questions take weeks because the data isn’t joined up
- AI projects stall because the data isn’t ready
What you receive
- Data pipelinesScheduled or incremental loads from each source, with error handling and alerts.
- Governed data modelRaw, cleaned and business-ready layers with agreed definitions.
- Quality checksRules that stop bad records reaching reports, with an issue list to resolve them.
- Documentation and runbooksHow it works, how to change it and what to do when something fails.
Typical use cases
Each project starts with an agreed way to measure success.
Single view of sales and finance
Combine CRM pipeline, invoices and payments so revenue reporting matches across teams.
Measured by: reconciliation differences and time spent preparing month-end reports.
Operations data hub
Bring orders, inventory and fulfilment data together for daily operational reporting.
Measured by: data freshness and the number of manual exports removed.
AI-ready data
Prepare documented, permissioned data that data agents and AI workflows can use safely.
Measured by: coverage of agreed definitions and access rules.
How we deliver it
Assess
Review sources, current reports, data quality and the decisions the data must support.
Design
Agree architecture, layers, security and the first data products to deliver.
Build
Deliver source by source, with working data available early.
Hand over
Monitoring, documentation and training so your team can run it.
Technologies We Work With
We recommend the platform that fits your existing Microsoft or cloud investment, data volumes and skills.
- Microsoft Fabric (lakehouse, warehouse, Data Factory, notebooks)
- Azure Data Factory, Azure Synapse Analytics, Azure SQL, Azure Data Lake Storage
- Databricks and Snowflake where they suit your environment
- Python, SQL, Git and CI/CD for repeatable deployments
Product names are trademarks of their owners. TriAxiom Tech is independent and does not hold partner status with these vendors.
Business performance
Sales and finance model, fiscal year, revenue in US dollars
Revenue and target by month
Revenue by region
Product lines
| Product line | Revenue | vs. target | Gross margin |
|---|---|---|---|
| Subscriptions | $3.37M | +4.7% | 51.6% |
| Professional services | $2.41M | −2.5% | 33.8% |
| Hardware | $1.87M | +1.7% | 22.1% |
| Support plans | $1.26M | +7.9% | 57.6% |
| Total | $8.91M | +2.4% | 41.5% |
Days sales outstanding
| Month | Revenue | Target |
|---|---|---|
| Jan | $615K | $600K |
| Feb | $584K | $620K |
| Mar | $649K | $640K |
| Apr | $716K | $660K |
| May | $696K | $680K |
| Jun | $734K | $700K |
| Jul | $785K | $720K |
| Aug | $748K | $740K |
| Sep | $817K | $760K |
| Oct | $858K | $780K |
| Nov | $823K | $800K |
| Dec | $886K | $820K |
Business Performance Dashboard
A dashboard built on a governed model that combines sales, finance and regional data — the kind of output a data engineering project makes possible.
This is a demonstration built with synthetic data. It shows the kind of work we deliver; it is not a client project or a client result.
See all demonstrationsSupport and handover
How we work with you during and after the project.
- Project communicationA named contact, a shared plan, and milestone reviews at times that suit your time zone.
- Access and data handlingWe use accounts you create and control, least-privilege access, and remove it at the end of the work. Data stays in your environment wherever possible.
- Code and account ownershipCode, reports, models and cloud resources are created in your tenant or repositories and belong to you.
- Documentation and trainingWritten documentation and training sessions so your team can run and change what we build.
- Post-launch supportA support period after go-live, with optional ongoing support and optimization.
- Clear, separate costsOur fees are separate from software licenses, cloud consumption and advertising spend, which you pay directly to the provider.
Related services
Frequently asked questions
Do we need Microsoft Fabric?
No. Fabric is a strong option if you already use Microsoft 365 and Power BI, but we also work with Azure services, Databricks and Snowflake. We compare options with you and document the trade-offs.
How long does a first phase take?
It depends on the number of sources and how ready the data is. We agree a first, useful milestone during scoping so you see working data early.
Where will our data be stored?
In your own cloud tenant and the region you choose. We don’t copy your data to our own systems.
Can our team maintain it?
Yes. We use standard tools, version control and documentation, and include training as part of handover.
What does it cost to run?
Platform costs depend on data volume and usage and are billed by the cloud provider. We estimate them during scoping and design to keep them predictable.