Service
Data Engineering & BI
Data engineering is the work of getting information out of your systems, cleaning it, storing it in one place and making it available for reporting. Business intelligence (BI) is the layer on top: the dashboards and reports your teams read. We build both, with checks along the way so the numbers can be trusted.
What you get
- ETL or ELT pipelines that collect data from your systems on a schedule
- A data warehouse structured around the questions your teams ask
- Dashboards and self-service reports
- Data quality checks and monitoring that flag problems early
- Documentation of where each figure comes from
A good fit when
- Reports are built by hand in spreadsheets and nobody is sure they agree
- Data is spread across several systems and cannot be viewed together
- You want dashboards that update themselves and that people trust
How a project runs
Four steps, no surprises.
Each stage ends with something you can read, try or approve, so you always know where things stand.
Talk
You describe the problem. We ask questions until we both understand it.
You get
- A written summary of the problem and the goal
- Honest options and trade-offs
- A straight answer on whether we are the right fit
We need from you
A short brief, any existing systems and your deadlines.
Scope
We turn the conversation into a plan you can approve.
You get
- What gets built and in what order
- A cost and timing estimate
- Risks and assumptions listed up front
We need from you
Your sign-off on scope and priorities.
Build
Working software in short cycles, so you see progress and can change course.
You get
- Something you can try at the end of each cycle
- Regular progress updates
- Tested, documented code in your own repository
We need from you
Quick feedback and one decision-maker we can reach.
Support
We stay on to run, fix and extend what we shipped.
You get
- Deployment and a clean handover
- Monitoring, fixes and updates
- Room to add features or extra engineers later
We need from you
Report issues by email and we pick them up.
Common questions
What is the difference between ETL and ELT?
Both move data from source systems into a warehouse. ETL (extract, transform, load) cleans and reshapes the data before it is stored. ELT (extract, load, transform) stores the raw data first and transforms it inside the warehouse. The right choice depends on data volume, tooling and how the data will be used.
How do you keep the numbers trustworthy?
Pipelines include automated checks for missing, duplicated or out-of-range data, and monitoring that alerts the team when a load fails or a figure looks wrong.
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Discuss your project
Let's build what your business needs.
Tell us what you need. A few lines are enough, and we reply by email.