THE THROUGH-LINE
I have worked across data engineering, migration, analytics, machine learning, AI, software and commercial operations. The through-line is translating messy constraints into reliable data products, analysis and systems people can actually trust and use.
Career map
The chronology matters, but the transitions matter more: technical depth moved closer to business decisions, then back into enterprise systems with stronger delivery discipline.
Current role
Enterprise data engineering, integration and migration for banking clients across Africa and the GCC, with hands-on ETL, source-to-target mapping, transformation, profiling, cleansing, validation and reconciliation.
Client-identifying and proprietary implementation detail remains intentionally limited.Al Tayseer Group
Consolidated multi-brand and external-agency performance data across four group companies into a unified reporting view, aligning KPIs, timelines and deliverables for monthly commercial decision-making.
Al Tayseer Group
Built recurring sales, inventory, production and warehouse reporting, then aligned manufacturing and sales plans with raw-material and finished-goods availability.
Al Tayseer Group
Extracted ERPNext sales, inventory and warehouse data, defined KPI and reporting logic with operations and finance, and connected paid-social performance data with commercial outcomes.
Parallel tracks
These are not footnotes to a timeline. They sharpen communication, scoping and end-to-end ownership in ways a single job title does not show.
Orcas Online
Self-employed
Foundation
Nov — Dec 2023
Sep 2023
Aug 2023
Learning ledger
Education and credentials stay visible here because they help explain the range. They do not need their own homepage screens.
Education
Canadian International College
ExploreAI Academy / ALX / African Leadership University
Credentials
Databricks
Udemy · Ed Donner
McKinsey Academy
Coach Academy
Canadian International College
Operating stack
No percentages and no logo wall. The useful signal is the combination of domains, not a decorative count of technologies.
How I work
Start by understanding what is known, what is missing and what should not be claimed. Good systems become easier to build once the evidence boundary is explicit.
Turn unclear requirements, fragmented data and competing constraints into a shared model that technical and business stakeholders can reason about together.
Use the simplest architecture that can carry the real job, then add complexity only when the evidence or operating constraints require it.
NEXT
Choose the route that matches what you are evaluating.