Data Engineering
Numbers live in five tools, none of them agree, and every report starts with an hour of copy and paste. We connect those sources and build reporting you can use without reconciling it by hand.
We build the pipelines that move your data, the warehouse that holds it, and the models that define your metrics. You get the code and documentation to maintain them.
What we build
ETL and ELT pipelines
Pipelines that pull data from your apps and spreadsheets on a schedule. We add checks for missing records, failed imports, and changes to source fields, with alerts when something needs fixing.
Warehouse setup and modeling
A warehouse organized around your orders, customers, and other business records. We agree on how each metric is calculated so your reports use the same definitions.
Data quality and validation
Checks for duplicates, missing values, and totals that don't reconcile. These run with each load and flag problems for review.
Dashboards and reporting
Reports built from the same underlying data and refreshed on a schedule. Spend the morning using the numbers instead of assembling them.
How the engagement works
A typical first phase covers one pipeline and the data model for a specific report. We scope that work together, get it running, and use what we learn to decide what comes next.
- Audit your current sources and where the numbers break down
- Stand up the warehouse and the first pipeline end to end
- Model the core entities and add validation
- Wire up reporting and hand over documentation
Who this is for
For small teams spending hours each week combining exports, fixing spreadsheets, or trying to explain why two reports disagree.
Common questions
How soon will I see something working?
A first pipeline and data model usually take a few weeks. The timeline depends on access to your source systems and how much cleanup the data needs. We work that out during scoping.
Do I have to choose specific tools or a warehouse?
You don't need to choose tools before we talk. We review what you already use and recommend changes based on the data volume, reporting needs, and cost.
What happens after the build is done?
You get the code, documentation, and checks for data quality. Your team can take over maintenance, or we can stay on to handle monitoring and changes.
Related services
Once the data is in place, Workflow Automation can handle the steps around it. AI Implementation covers tasks such as searching records or processing documents.
Which report takes the most work to put together? Tell us what goes into it and where the numbers come from.