Pull from every source, on a schedule
ETL and ELT jobs connect to the systems you already run and bring their data into one place, on a schedule, with failures raised loudly instead of left silent.
Data engineering
We build the data foundations that make your analytics and AI reliable, instead of the demo that breaks in production.
Most teams do not have a model problem. They have a data problem. Reports disagree, the same customer appears three ways, and no one can say where a number came from. Analytics and AI inherit all of it.
We design and build the pipelines, models, and quality checks underneath. The honest first project is often fixing the data, so everything you build on top of it can be trusted.
Scattered data, numbers no one trusts
Data usually lives in too many places at once. A booking system here, a spreadsheet there, an accounting tool that exports a slightly different format every month. Pulling it together by hand works until it does not, and the people who understand the joins are the ones you can least afford to interrupt.
The cost shows up quietly. A dashboard nobody trusts, a forecast built on a column that changed meaning last quarter, an AI feature stalled because the training data is half empty. Before any of that improves, the foundation has to be sound and you have to be able to see how each number was produced.
From source to served, in four stages
The same four stages every reliable pipeline passes through. Scroll the rail to follow data from a raw source to a number you can stand behind.
Source to served
The layers underneath your reports
Each piece exists so the one above it can be trusted. We build them in order, from the pipe that moves the data to the foundation your AI eventually learns from.
- Pipelines that run themselves
- We build ETL and ELT pipelines that pull from your sources on a schedule, handle failures loudly, and stop quietly corrupting data the moment something upstream changes.
- Warehouse and lakehouse modelling
- We design the warehouse or lakehouse layer so one customer is one customer and every metric has a single agreed definition you can point to.
- Data quality you can see
- We add checks that catch missing fields, broken joins, and out of range values at load time, before they reach a report or a model.
- Lineage and documentation
- Every field traces back to its source, so when a number looks wrong you can follow it to where it came from instead of guessing.
- A foundation built for AI
- We shape the data with the next step in mind, so predictive models and document workflows have clean, consistent inputs to learn from.
Catch bad data at the gate, while it is cheap to fix
Bad data is cheapest to fix the moment it arrives. We put the checks at load time and keep a trail back to the source, so a wrong number is something you can trace, not something you argue about.
At load timepass / fail
Missing fields caught at load
Rows arriving without the columns a report depends on are flagged before they reach it, not after someone notices the total looks low.
Broken joins surfaced loudly
When a key stops matching between systems, the run fails visibly rather than quietly dropping records and skewing every count downstream.
Out of range values held back
A negative quantity or a date in the wrong century is caught at the gate, so the strange number never makes it into a dashboard or a model.
Lineage
Follow a number to where it came from
When a figure looks wrong, you trace it back through each step instead of guessing.
- Dashboard metric
- Modelled table
- Transform step
- Raw load
- Source system
Audit first, then build what holds
Audit
We map where your data lives, how it moves, and where it currently breaks, then write down the specific problems worth fixing first.
Design
We agree the target model, the definitions, and the quality rules with your team before any pipeline is built, so the structure matches how you actually work.
Build
We build the pipelines, the warehouse layer, and the checks in reviewed increments, testing against real data rather than a clean sample.
Hand over
We document the model and lineage, walk your team through it, and leave you able to run and extend it without us in the room.
Held to a production standard
Data discipline turns a day of period-close into about twenty minutes of review, in the same accounting pipeline we bring to your foundation: data kept clean, deduplicated, and lineage-traced back to its source. The product behind that pipeline is Pileform. Strong data foundations are usually the first phase of a wider effort, which is why this work sits close to our digital transformation engagements.
What you keep when we leave
Working pipelines
Scheduled ETL or ELT jobs connecting your sources to a central store, with alerting when a run fails instead of silent gaps.
A modelled warehouse or lakehouse
A clean structure with agreed definitions, deduplicated entities, and tables your analytics tools and your team can read directly.
Quality and lineage checks
Automated tests at load time and a clear trail from every field back to its origin, so you can verify a number rather than trust it blindly.
Documentation and handover
Plain written docs of the model, the rules, and how to operate it, plus a walkthrough so your team owns it after we leave.
Data foundations, answered plainly
Do we need a data warehouse, or can we keep using spreadsheets?
Spreadsheets are fine until several people depend on them and the numbers start to drift. If reports disagree or a single export breaks your month, a modelled store is usually worth it. We will tell you honestly if you are not there yet.
Will this work with the tools we already use?
Yes. We connect to the systems you have rather than asking you to replace them. The goal is to bring your existing sources together cleanly, not to start over.
What happens when our source systems change?
Source changes are the main reason pipelines quietly break. We build checks that detect when an upstream format shifts and surface it loudly, so you find out before a bad number reaches a report.
How is this different from your predictive analytics work?
Data engineering is the foundation; predictive analytics is what you build on it. Models are only as good as their inputs, so this work usually comes first. The two often run as one engagement. See /services/predictive-analytics.
Do you hand it over, or do we depend on you to keep it running?
We hand it over. We document the model and lineage and walk your team through operating it. You are free to keep us on for changes, but you should never be stuck because only we understand it.
How long does a first engagement take?
It depends on how many sources you have and how tangled they are. We scope the audit first so you get a clear picture of the work before committing to the full build.
Data engineering is the foundation predictive work is built on. See predictive analytics.
Start with the foundation
If your reports disagree or your AI plans are stalled on messy data, the first step is a clear look at what you have. We will tell you what is solid, what needs fixing, and what it would take to put it right.