AA•ABDULRAHMAN
ALZAHRANI
ENالعربية
ENGINEERING EXPERIENCE. A DATA-DRIVEN FUTURE.

Real-world
operations.
Reliable data.
Practical AI.

I’m Abdulrahman Alzahrani.
A data engineering lead connecting hands-on operations, analytics and business-focused problem-solving, with a focus on Saudi Arabia’s digital future.

Explore my workMeet the person behind the CV
AL KHOBAR, SAUDI ARABIAENERGY · DATA · BUSINESS
Abstract geological layers transforming into luminous structured data traces
FIELD EXPERIENCEDATA INTELLIGENCE
FIELD / DATA

Understand the operation.
Then improve the system.

7+ yearsEngineering & operational experience
Halliburton2019–present · Field & Digital Center
MSc Artificial IntelligenceUniversity of Leeds · In progress
01 THE THROUGH-LINE

Good technology starts
with understanding
the real problem.

My career began where the data is created: in the field.

As a wireline field engineer, I learned to make decisions under pressure, work closely with clients and understand what reliable operational delivery really takes.

In Halliburton’s Digital Center, that perspective became a foundation for leading data quality and delivery work. Today, I’m building on it through an MSc in Artificial Intelligence, connecting technical methods with problems that matter to the people using them.

I’m building on my experience in operations and data through my studies in AI, bringing together an understanding of how work gets done and how technology can improve it. This path aligns with the Kingdom’s direction toward a knowledge- and innovation-driven economy under Saudi Vision 2030.

Operations gave me context.
Data gave me a different way to improve it.

01 / OPERATIONS

Close to the work.

Wireline field engineering, client coordination and delivery in demanding environments.

02 / DATA LEADERSHIP

Trust in the data.

Well-log data preparation, quality control and digital delivery, alongside team coordination.

03 / APPLIED AI

Build on the foundation.

Postgraduate study and academic projects in machine learning and data analysis.

02 SELECTED WORK

A closer look at
how I work.

Professional experience and academic projects.
Different settings. The same practical mindset.

01
PROFESSIONAL EXPERIENCE

Data quality at the operational edge

Digital well-log preparation, validation and delivery

CONTEXT

Operational datasets need to be dependable before they can support analysis or decisions. My field background helps me understand what the data represents, as well as how it is processed.

MY CONTRIBUTION
  • Lead a data-engineering team working on digital well-log datasets.
  • Support extraction, cleaning and validation of well-log datasets.
  • Work across teams on depth matching, environmental corrections and delivery requirements.
  • Support team capability through onboarding and training.
THE CONNECTION

Domain understanding makes technical quality checks more meaningful.

02
ACADEMIC PROJECT

Insurance fraud classification

Applied machine learning for a classification problem

PROJECT FOCUS

Prepared and explored insurance data, engineered features and compared logistic regression with random forest classification.

WHAT IT DEMONSTRATES

Evaluated models with cross-validation, precision, recall and F1, considering the business costs of different errors. Built with Python, pandas and scikit-learn.

University of Leeds · Academic project · 2026

THE CONNECTION

A model needs an explanation as well as a prediction.

03
ACADEMIC PROJECT

University rankings analysis

Exploring data, patterns and the questions behind a ranking

PROJECT FOCUS

Cleaned university-ranking data, handled missing values and used visual comparisons to investigate trends and correlations.

WHAT IT DEMONSTRATES

Interpreted findings alongside the limitations of the dataset. Built with Python, pandas, Matplotlib and Seaborn.

THE CONNECTION

Useful analysis helps people understand what a number means.

03 WHERE I ADD VALUE

Three perspectives.
One connected approach.

Explore the experience I bring to a team.

Make the data dependable.

Experience with data extraction, cleaning, validation and digital well-log delivery. I connect data quality requirements with the operational context behind them.

Data qualityValidationWell-log processingCross-team delivery
04 ALWAYS BUILDING

Engineering roots.
Continuing to grow.

IN PROGRESS

MSc Artificial Intelligence

University of Leeds

DEGREE

BSc Electrical Engineering

California State University, Fresno

PROFESSIONAL DEVELOPMENT

Leadership Principles & CORe

Harvard Business School Online

TECHNICAL FOUNDATION

Data Science Immersive

General Assembly

05 START A CONVERSATION

Let’s turn experience
into what’s next.

For opportunities connecting data, AI, operations and business improvement.

appdulrahman@gmail.com
Get in touchLinkedIn