Submission-ready reporting support
Models and dashboards where definitions, lineage and caveats are clear enough to defend.
Pharmaceutical
Governed analytics you can explain later
Regulated work needs more than a useful dashboard. The access, lineage, definition and review trail must still make sense when the question comes back months or years later.
Common parts of the stack
- Data layer
- Snowflake · dbt · AWS
- Workflow
- Airflow · Terraform
- Reporting
- Tableau · Power BI
The problem
What makes pharma analytics harder
The work still has to answer a business question. It also has to survive review, access checks and a future reader who was not in the room.
The number needs a trail
A chart is not enough if nobody can show where the metric came from, who changed it, and which rule was used.
Access is part of the design
Teams need to move without turning every request into a permission exception or a manual extract.
Review comes too late
Governance added after the dashboard is built usually means rework. The cheaper route is to design it into the model.
The approach
Build the controls into the pipeline
We treat governance as part of the data product, not a document written at the end. The model, tests and reporting layer are built so a future review can follow the logic without relying on memory.
Traceable definitions
Metric rules, source fields and known limits are documented where the model is built, not only in a slide deck.
Tested handover
The pipeline includes checks for the rules that matter, so defects are caught before a stakeholder finds them.
Controlled access
Roles and ownership are designed with the reporting need, instead of being patched around it later.
Where we can help
These are the common shapes behind governed analytics work in life sciences.
Commercial analytics
Revenue, access and performance reporting that ties back to agreed metric rules.
Platform and handover
Pipelines, documentation and ownership designed so internal teams can run the work safely.
What clients say
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
He has contributed above expectations to the service area he works in.
Not only has he demonstrated deep domain knowledge around Tableau, data visualization and integration, but he also provides frequent input on how the team can improve service delivery and increase value for internal customers.
Dick Olsson
Director, Principal Engineering Lead, Pfizer
I wholeheartedly recommend Igor as a top-notch data engineer. His ELT pipelines are nothing short of magic, his API crawlers are like digital detectives, and his Tableau skills bring data to life.
Igor brings an infectious enthusiasm that turns every challenge into an adventure. He thrives on positive debates, adding a dash of excitement to our discussions, and his love for the outdoors infuses fresh energy into the team. His technical prowess is matched only by his ability to make data fun and engaging.
Mubarak Olayinka
Product Manager, Pfizer
How an engagement usually runs
Agree the review standard
We start by naming what the work must prove, who will read it, and what evidence has to travel with it.
Map the data path
Sources, transforms, access and reporting are traced before the model is changed.
Build with tests and docs
Controls are added while the pipeline is built, so review is not a separate clean-up phase.
Hand over the operating model
Your team gets the runbook, owners and checks needed to keep the work reliable after we leave.
Questions regulated teams ask early
Can you work within our existing controls?
Yes. The point is not to bypass your controls. It is to build the data work so the controls are easier to meet.
Will this slow the project down?
Good controls add thought at the start and remove rework at the end. Retrofitting them is usually slower.
Can you name previous pharma clients?
We have delivered reporting and pipeline work inside Pfizer teams. We name references on a call, once we know the permission position on both sides.
Next step
Which number do you not trust?
Bring the report nobody trusts. We will tell you what is actually wrong underneath it.
Thirty minutes, straight to the problem. No deck, no pitch, and a written summary afterwards either way.