Lagos, Nigeria

Pharmacist & Data Engineer
for African Healthcare

I build data infrastructure and analytics systems for pharmaceutical and life sciences organisations across sub-Saharan Africa — bridging clinical insight with technical execution.

Python Power BI Microsoft Fabric SQL Pharma Data Health Analytics HEOR Power Query (M)
Olayinka Akerekan

About me

A rare cross-disciplinary profile

Pharmacy background

B.Pharm from the University of Ibadan. Clinical context that most data practitioners simply don't have.

Data engineering

Building pipelines, drug dictionaries, and analytics systems for pharmaceutical CROs and consulting teams.

Health analytics

Power BI dashboards, Microsoft Fabric, and market intelligence for life sciences decision-makers.

Sub-Saharan focus

Deep knowledge of Nigerian and Ghanaian pharma markets, NAFDAC data, and regional drug registries.

I'm a data engineer and analytics professional working in the pharmaceutical and life sciences consulting space across sub-Saharan Africa. My work sits at the intersection of pharmaceutical science and modern data engineering — I bring clinical literacy to data problems that most engineers can't read, and technical rigour to health questions that most pharmacists can't compute. I'm building toward Health Economics and Outcomes Research (HEOR) as my next professional layer.

Selected work

Projects

Classification

Maternal health supplement classifier

Formal classification system for ~54,000 rows of supplement data, aligned to WHO/UNICEF standards. Covers IFA, MMS, UNIMMAP, and related categories with audit variable columns and iterative logic corrections.

Python Pandas WHO/UNICEF standards
Dashboarding

Nigerian vaccine market dashboard

Power BI semantic model and DAX measures for MenC, Hexavalent, PCV13, and TLD market intelligence across Nigerian regions. Built on Microsoft Fabric with Direct Lake architecture.

Power BI DAX MS Fabric
Data quality

Ghana drug dictionary QC pipeline

Five-point quality check across 72,187 rows — covering duplicate SKUs, missing company values, ATC inconsistencies, constituent-vs-ATC mismatches, and conflicting presentations. Delivered with colour-coded Excel reports and an interactive dashboard.

Python Fuzzy matching Excel
Pipeline

Pharma sell-out data integration

Power Query (M) solution consolidating multiple Excel workbooks from SharePoint into a standardised 19-column dataset for a consulting team, with model-layer column harmonisation across sources.

Power Query (M) SharePoint Excel
Classification

NAFDAC brand classification pipeline

Five-strategy cascade matching system for local/imported classification of Nigerian pharmaceutical brands against the NAFDAC Green Book, with a full methodology document and colour-coded confidence scoring.

Python NAFDAC data Fuzzy matching

Thinking out loud

Writing

Let's connect

Get in touch

Whether you're building health data systems in Africa, exploring HEOR, looking for a speaker on pharma AI, or just want to talk data — I'm reachable.