Data Engineering & Analytics
Numbers you can actually trust
Every report is only as good as the data underneath it. I build the pipelines that pull your data together, clean it, check it, and keep checking it — so when a number looks wrong, you find out from the system rather than from a customer.
What you end up with
- One clean, trusted set of numbers instead of five conflicting exports
- Data problems caught by automated checks, not by an angry customer
- Reports that rebuild themselves on schedule and land where they're needed
- Analysis that takes minutes instead of a week of spreadsheet wrangling
- A history you can actually look back through and compare
Signs you need this
If two or three of these sound familiar, it is probably worth a conversation.
- Two reports on the same thing give two different numbers
- Analysis means exporting to a spreadsheet and working through it by hand
- Nobody can say confidently which figure is the correct one
- A data problem is usually discovered by a customer rather than by you
- Your reporting depends on one person who knows where everything lives
What is actually delivered
Not a statement of intent — the concrete artefacts and outcomes you receive.
Pipelines that run themselves
Scheduled jobs in Python or R that pull from your databases, files and APIs, transform the data properly, and load it somewhere reliable — with alerts when a run fails instead of silence.
Data quality checks
Automated tests on every load: are the totals right, are the keys unique, did today's volume look sane, did anything change shape? Bad data gets stopped before it reaches a report.
A proper analytics model
Your transactional data reshaped for analysis, so questions get answered in seconds and everyone's numbers agree because they come from one place.
Exception and alert jobs
Scheduled analysis that hunts for the things you'd never spot by eye — unusual patterns, misuse, pricing errors, missed collections — and emails the right person automatically.
Dashboards and scheduled reports
The numbers you actually decide on, delivered on a schedule to the people who need them, in a format they'll actually open.
How the engagement runs
- 01
Audit
Find every place data lives today and check honestly how trustworthy each one is.
- 02
Model
Agree what each number means, once, so the definitions stop drifting between teams.
- 03
Build
Pipelines with validation, logging and alerting built in from the first run.
- 04
Watch
Quality checks and monitoring so problems surface on their own, early.
Data Engineering: common questions
A BI tool draws charts from whatever you feed it. If the underlying data is inconsistent, you get beautiful charts that disagree with each other. This work is the layer underneath — making sure the numbers arriving at the tool are correct, consistent and defined once.
You hear about it immediately rather than discovering it in a report three weeks later. Every job logs what it did, alerts on failure, and where possible retries safely. Silent failure is the thing these pipelines are specifically designed to prevent.
Yes, and usually that is the right answer. Most businesses do not need a new platform — they need their existing database modelled properly, with scheduled jobs and validation around it.
Almost always because the same word means different things in different places — one team counts an order when it is placed, another when it is dispatched. The fix is not a better dashboard, it is agreeing the definitions once and building every report from one modelled source.
Both, and I pick per job rather than by habit. R is excellent for analysis, statistics and reporting pipelines; Python suits general engineering, APIs and anything heading towards machine learning. Plenty of my production pipelines are R talking to PostgreSQL on a schedule.
Probably not yet. A well-modelled schema in the database you already run, plus scheduled jobs and proper validation, covers most businesses for a long time. I would rather make your existing database trustworthy than sell you a platform you do not need.
Data Engineering in your industry
The problems look different in each of these, so the way I approach them differs too.
Pharmacy & healthcare retail chains
Built for batch, expiry, licences and thin margins
Retail & multi-store businesses
One live view across every branch
Money transfer, forex & remittance
Where compliance and reconciliation cannot slip
Manufacturing & engineering
From whiteboard planning to real production data
Distribution & wholesale
Volume, credit and schemes, under control
D2C brands & online sellers
One stock truth across every channel you sell on
Services that usually go with this
ERP Implementation & Consulting
From selection to go-live, without the six-figure disaster
Business Process Automation
Remove the manual work that quietly eats your margin
Accounting, Reconciliation & Revenue Assurance
Stop losing money you never knew you were losing
Want a straight answer on your situation?
Thirty minutes, no pitch. I'll tell you what I would do and what it costs.