Analytics Engineer IItalabat · Dubai, Dubai, United Arab Emirates

Analytics Engineer II

🗓️ Posted 2026-10-18 Dubai, Dubai, United Arab Emirates Full-time Hybrid ICT
💰 Salary Not shared 📊 Market AED 15,000–AED 25,000/mo

About the Company

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat's local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region. We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily. Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner. Job Description As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users. As an Analytics Engineer, you will be responsible for transforming raw data into well-structured, reliable, and accessible data models that enable analysts, data scientists, and business stakeholders to make informed decisions. You will work closely with data engineers to build scalable data pipelines and collaborate with an

Requirements

  • ● Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
  • ● + years of hands-on experience in data modeling using dbt.
  • ● Strong proficiency in Python, SQL and experience with dbt for data modeling.
  • ● Familiarity with version control (Git) and CI/CD practices for data workflows.
  • ● Understanding of data engineering principles, including ETL/ELT processes.