Tarik El Lel, Developer in Dubai, United Arab Emirates
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Tarik El Lel

Verified Expert  in Engineering

Machine Learning Developer

Dubai, United Arab Emirates
Toptal Member Since
October 4, 2022

Tarik is a data engineer and analyst with over six years of experience working with leading technology companies in Dubai, such as Farfetch, Starz Play, and OLX. He is skilled in data architecture, database modeling, ETL, and machine learning (ML). Tarik holds a master's degree in computer science with big data analytics from the University of York. His passion for empowering teams to use data creatively to solve challenging business problems makes him an efficient engineer.


Python, SQL, Apache Airflow, Data Modeling, PostgreSQL...
Big Data, Data Engineering, SQL, Python
Analytics, Python, SQL




Preferred Environment

Python, SQL, Apache Airflow, Kubernetes, Docker, Data Build Tool (dbt), Amazon Web Services (AWS), Google Cloud Platform (GCP), Looker, Git

The most amazing...

...ETL pipeline I've built is a size recommendation pipeline for an eCommerce clothing platform with ML using Word2vec and random forests to decrease returns.

Work Experience

Analytics Manager | Analytics Engineering Lead

2018 - PRESENT
  • Managed, designed, developed, and launched into production customer retention and cohort analysis data models and dashboard using BigQuery, Airflow, data build tool (dbt), and LookML.
  • Designed and automated notification services for the customer excellence team using Docker and Airflow; this enabled A/B testing in different engagement strategies for customer advisors.
  • Built data pipelines with a data science team for size and fit prediction algorithms to decrease sizing-related returns.
  • Led the team to create all necessary data models and dashboards for the newly established an EMEA (Europe, the Middle East, and Africa) growth team.
Technologies: Python, SQL, Apache Airflow, Data Modeling, PostgreSQL, Google Cloud Platform (GCP), Data Build Tool (dbt), Looker, Git, Databases

Digital Marketing Analyst

2015 - 2018
  • Built reporting pipelines using Google Data Studio and PostgreSQL.
  • Created reports, executed ad-hoc analysis, and conducted weekly trade meetings using Google Analytics, Tableau, Mixpanel, and AppsFlyer.
  • Developed the A/B testing pipeline for measuring and evaluating experiment performances.
Technologies: Big Data, Data Engineering, SQL, Python

Digital Marketing Analyst

2013 - 2014
  • Analyzed the paid search and search engine optimization (SEO) performance using Google Analytics, AppsFlyer, Google Search Console, and Google Adwords (Google Ads).
  • Performed ad-hoc analyses to understand digital marketing performance across different OLX portals in the Middle East and North Africa (MENA).
  • Managed organic and paid search performance for OLX and dubizzle across MENA markets.
Technologies: Analytics, Python, SQL

Data Modeling Customer Retention and Lifecycles

The project required the development of data models and ETLs to enable analyses of customer lifetime metrics over time, including cohorts, retention, and repurchase metrics. The project included designing models and implementing them in the data build tool (dbt) and BigQuery and scheduling them using Apache Airflow. Data visualization modeling was then done using LookML on Looker. The processes allowed measuring customer lifetime value over time and understanding how the retention metrics changed across different markets.

Customer Service Notification System

Built a notification service that would pull daily customer data, parse any relevant behavior, and generate a ticket for customer service to action. The project included the addition of A/B testing to the service that measured which actions would work best. ETL was created to record all notifications or tickets and to measure experiment success. The stack used on the project was Google Compute Engine, Docker, Airflow, BigQuery, and Salesforce.

Size and Fit Prediction Algorithm

This python-based service would recommend user sizes on product listing pages based on their previous shopping history to minimize size-based returns. I worked with the data science team to build the ETL to prepare data for ingestion by the deep learning algorithm for the daily training of the model. I also designed one of the prediction algorithms, which used Word2vec for feature generation and random forest for prediction.

ETL Pipeline for Supply and Orders for an eCommerce Website

The project was a business-to-business ETL pipeline from a source database and weblog. It ran containers for the source transactional database, destination analytical database, Airflow scheduler, and a lightweight database client, resulting in a source Postgres database model for the customer. The project also included the establishment of a destination database, pipeline, and model to meet the customers' analytics requirements, including an ELT from the source Postgres database to the destination Postgres database.

Deep Learning Model for Detecting COVID-19 from Chest X-Rays

As a part of the final project during my master's degree, I designed an ensemble deep learning model that used three convolutional neural networks and a classifier ensemble layer to detect whether a chest X-ray belonged to a patient with COVID-19. The model achieved 95.66% accuracy.
2019 - 2021

Master's Degree in Computer Science

University of York - York, United Kingdom

2007 - 2010

Bachelor's Degree in Business Administration

American University of Beirut - Beirut, Lebanon


Deep Learning Specialization

DeepLearning.AI | via Coursera


Apache Airflow, Looker, Git, BigQuery


Python, SQL, Bash


Docker, Amazon Web Services (AWS), Google Cloud Platform (GCP), Kubernetes, Salesforce


ETL, Data Science


Databases, PostgreSQL


Data Build Tool (dbt), Data Engineering, Big Data, Machine Learning, Data Modeling, Software Engineering, Computer Science, Deep Learning, Analytics, Google Search Console

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