Data engineering for SaaS and e-commerce
Your CRM, billing and finance tools each give a different answer. We build the data layer underneath so the numbers agree, and they stay agreed after we leave.
The problem
When reports are stitched together by hand, every new one adds another version of the truth. These are the moments that usually get someone to call us.
Usually it starts with a director who is fed up. Reports disagree, nobody can say which one is right, and every fix is another workbook on top of the last one.
The syncs and scripts they built keep running until one quietly stops. Nobody else knows how they work, and a new hire takes months to find.
What you got is a dashboard per team, each with its own idea of pipeline, revenue or churn. What is missing is a semantic layer: one set of definitions that every report reads from.
Which channels bring customers who stay? Why did churn move last quarter? The answer is spread across your CRM, billing, product and ad data, and joining it by hand takes longer than the decision can wait.
Who we work with
The pattern is the same each time: the numbers that should agree live in several systems, and each one says something different. We fix the layer underneath so the business can trust one answer.
SaaS revenue numbers live in several places. The CRM has the forecast, billing has what was invoiced, and finance has the board version. Each one is defensible, but they do not always agree.
We turn MRR, churn, expansion and coverage into tested pipeline logic, so monthly reporting starts with the number rather than a reconciliation.
Revenue reporting rebuilt from source systems to dashboard
Ecommerce stacks grow fast: storefront, payments, subscriptions, email and ad platforms, each with its own customer and conversion numbers.
We bring them into one customer model, so cohorts, retention and channel spend can be compared on the same basis.
Promo code analytics that added 11% to a D2C bottom line
Pharmaceutical teams need more than a dashboard. Access must be provable, lineage traceable, and a number in a submission must still make sense years later.
We build that control into the model and the pipeline from the start, so review is part of the work rather than a clean-up job at the end.
Pfizer reporting and pipeline work
SaaS revenue numbers live in several places. The CRM has the forecast, billing has what was invoiced, and finance has the board version. Each one is defensible, but they do not always agree.
We turn MRR, churn, expansion and coverage into tested pipeline logic, so monthly reporting starts with the number rather than a reconciliation.
Revenue reporting rebuilt from source systems to dashboard
Ecommerce stacks grow fast: storefront, payments, subscriptions, email and ad platforms, each with its own customer and conversion numbers.
We bring them into one customer model, so cohorts, retention and channel spend can be compared on the same basis.
Promo code analytics that added 11% to a D2C bottom line
Pharmaceutical teams need more than a dashboard. Access must be provable, lineage traceable, and a number in a submission must still make sense years later.
We build that control into the model and the pipeline from the start, so review is part of the work rather than a clean-up job at the end.
Pfizer reporting and pipeline work
What we do
Most engagements start small and grow. You do not have to buy all of it to begin.
One definition per metric, written down, and a model that produces it. Pipeline, bookings, revenue and churn are worked out once and used everywhere.
Data moves from your CRM, billing and product systems into a warehouse on a schedule, with tests that fail loudly. Built on dbt, Snowflake and AWS. No manual steps.
Tableau and Power BI, built around the questions your team really asks. Fewer dashboards, and each one answering something specific.
Documentation, tests and recorded training so your team owns it afterwards. The aim is that you stop needing us. That is not a slogan. It is how the work is scoped.
A call
Twenty minutes on the problem. Not a pitch. Sometimes the answer is that you do not need us, or that someone else is a better fit.
Scope and a price
A written plan with a number attached, before any work starts. No open-ended day rates.
Build, in the open
You see progress every day. Nothing lands as a surprise at the end.
Handover
Documentation, training, templates, recordings. Your team runs it. We are there if something breaks.
Three engagements, and what actually changed
E-commerce promotions and retention
Manual, a UK men's health brand, could not trust its numbers after four consultants left its data broken. A team of two rebuilt it in three months and built promo code analytics that found discounts leaving money on the table.
AI analytics layer
A mid-size SaaS company wanted AI on top of its analytics instead of a bigger BI team. Deploying and calibrating data.Ask took three weeks. It involved building the accuracy and trust, reconciling the definitions and logic, and building the further governance plan.
GitHub license management
By integrating GitHub, HR, and SSO data into a unified Tableau dashboard, a Fortune 100 Enterprise uncovered inactive users and streamlined license management. Two reviews so far have saved over $1.1M a year.
Tools we work with most
Not on the list? We start from the stack you already have.
Consistently brought strategic thinking on architecture, data mesh, governance, and cost optimization, reducing our dashboard delivery from weeks to days.
Proactively solved issues, bridged tech and business communication, and strengthened our internal capability to scale independently. I’d confidently recommend Igor to any pharma leader looking for strategically aligned, sustainable data solutions.
Simon Skurikhin
Data Engineer, Pfizer
Igor worked with us at Manual as an independent Tableau contractor. He clearly knows Tableau inside out and built us some great dashboards.
He was pragmatic, understood the balance of £ vs speed, and was willing to adapt his designs and approach appropriately. He was able to work independently and great at managing his own workload - a pleasure to work with.
Mark Madge
Head of Data, Manual
Before I worked with Igor, I said dashboard was “the D word.” He often anticipates what I would need.
I review and think “but what about… “ and he’s already thought of it. I focus on giving him the priorities; the questions we need to answer; and for what audience.
And he delivers every time. I feel overall more capable and effective due to Igor’s work.
He’s a joy to work with. I’m very happy to recommend him.
Heather McNamee
Service Manager, Techoir
Free workflow
data.Ask asks your Tableau, your database and your documentation the same question, then cross-checks the three answers and shows you where they disagree. It is the quickest audit of the problem this page describes, and it is free to use.
No call required to download it. You can run it in one day. If it shows you something you do not like, that is a good enough reason to talk.
A walkthrough from a Tableau Conference talk.
No. We do the engineering and hand over tested, documented models your developers can look after. If you hire a data person later, they start from something that works.
If your data were all in order, you wouldn't need help. That's where we come in. We start with the few numbers you argue about most, agree what they mean, and build them once in one place that every report reads from. The rest follows piece by piece, at a pace that suits you.
No. You see a scope and a price before any work starts, fixed or flexible while the scope takes shape. Most clients start with one small piece, so they see how we work before committing to more.
A first project is usually $7,000 to $10,000: a single delivery, or an in-depth assessment of your data and systems. Anything larger is scoped with you after a call.
Yes, where it is needed. Salesforce and HubSpot objects, fields and reports are all fair game. Our specialism is the layer below, and that is usually where the problem turns out to be.
No. That is the usual starting point, and it tends to surface the questions worth solving next.
Yes. Regulated delivery is a large part of our history, including work inside Pfizer teams.
Not ready to talk yet
One email a month on what actually worked and what did not, from real Tableau, governance and data engineering work. No sequence, no drip.
Next step
Bring the report nobody trusts. We will tell you what is actually wrong underneath it.
Twenty minutes, straight to the problem. No deck, no pitch, and a written summary afterwards either way.
