Data lives in separate tools, in different formats, and nobody trusts the numbers when they are pulled together.
15 / Data Engineering
Data Engineering
Clean, connected data that reports and AI can rely on.
Pipelines, warehouses and analytics that make your data usable and trustworthy.
We trace each report back to its source systems, fix the most trusted metric first, and build the pipeline with tests at every stage. Definitions are agreed in writing so two dashboards stop showing different totals.
What the build includes
A complete production workflow.
Source-to-warehouse pipelines
Scheduled jobs that pull from your apps, databases and files into one store, with retries and alerts.
Modelled reporting tables
Clean, documented tables for revenue, customers and operations that analysts can query directly.
Data quality tests
Automatic checks for duplicates, missing values and late loads, with failures sent to the owner.
Metric dictionary
A shared definition of each number, who owns it and how it is calculated.
Where it applies
Typical data engineering work.
- →One view of sales across CRM and billing
- →Replacing spreadsheet exports with a nightly load
- →Feeding clean history to a forecasting model
- →Combining ad spend with revenue
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
PostgreSQLSnowflakeDatabricksPythonAirflow
Industries
Expected outcomes
- →One agreed number for each metric
- →Reports that refresh without hand work
- →Problems caught before the board meeting
- →Analysts spend time analysing
- →Data ready for AI projects
Common questions
Before we scope it.
Which warehouse should we use?
We compare PostgreSQL, Snowflake, BigQuery and Databricks against your volume and team skills. Smaller data often needs less than people expect.
Can you work with our existing tools?
Yes. We build around what you already run, and replace parts only when they are the cause of the problem.
How do you handle changing source systems?
Pipelines check the shape of incoming data and alert on changes, so a renamed field is noticed immediately rather than weeks later.
Keep exploring
Related services and technologies.
Free Audit
Get my free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.