Analytics Engineer – Data Modeling and Analytics – Talabat

talabat is hiring an Analytics Engineer to help build reliable, scalable, and accessible data infrastructure that supports decision-making across the business in Dubai. This role sits between data engineering and analytics, focused on making sure data is modeled well enough that it’s easy to discover, understand, and actually use by teams across the company.

The work involves designing and maintaining data models, building and optimizing data pipelines, and working closely with analytics, product, and business teams to understand what data they need and how it should be structured to be genuinely useful rather than just technically correct. A core part of the role is making data self-service-friendly, so that business teams aren’t bottlenecked waiting on engineering for every new question.

Because talabat’s business spans multiple markets and verticals, the data modeling challenges here involve real complexity — reconciling data from different sources and markets into coherent, trustworthy models that different teams can rely on consistently.

This role suits someone with strong data engineering or analytics engineering experience who cares as much about making data understandable and discoverable as about making the underlying pipelines technically sound.

Good decisions across a company the size of talabat depend on good data being genuinely accessible, and an Analytics Engineer's job is to make sure that data isn't just technically present somewhere in a warehouse but actually modeled in a way that business teams can discover, trust, and use without needing an engineer to translate it for them every time. Poorly modeled data creates a hidden tax across the organization — every team that has to work around confusing or inconsistent data models is spending time and judgment on data wrangling instead of actual analysis, and that cost compounds across a company with talabat's scale and number of markets. When this role is done well, the effect is largely invisible: business and product teams simply get answers to their questions faster and trust those answers more, because the underlying data foundation is solid. When it's done poorly, the effect shows up everywhere as slow analysis, conflicting numbers between teams, and growing reliance on ad hoc engineering support that doesn't scale. Because talabat operates across multiple markets and business lines, the data reconciliation and modeling challenges this role solves are genuinely non-trivial, and solving them well is a direct enabler of faster, more confident decision-making throughout the business, from product teams to senior leadership.

Analytics engineering is one of the fastest-growing and most in-demand specializations in the modern data field, sitting at a valuable intersection between data engineering and business analytics, and building real expertise in it at talabat's multi-market scale is strong, differentiated experience for a data career. The data modeling challenges at a company like talabat — reconciling diverse data sources across markets and verticals into coherent, trustworthy models — are exactly the kind of complex, real-world problems that distinguish a strong analytics engineering resume from one built on smaller, simpler datasets. This role also builds a genuinely broad skill set, combining hands-on technical work (SQL, data pipelines, transformation tools) with the communication and business-partnering skills needed to make data genuinely usable, which is precisely the combination that senior data and analytics engineering roles are looking for. talabat's position as a leading regional consumer technology platform means strong work here is visible and valued, creating a credible path toward senior analytics engineer, data engineering lead, or analytics platform leadership roles over time. The skills and tools used in this role — modern data transformation frameworks, cloud data warehousing, data modeling best practices — are also highly portable across the broader tech industry, meaning experience gained at talabat travels well to future opportunities. Compensation for strong analytics and data engineering talent in the regional tech sector is also competitive, reflecting how much demand there currently is for this specific skill set.

• Bachelor’s degree in computer science, data engineering, or a related technical field
• Proven experience in analytics engineering, data engineering, or a closely related data modeling role
• Strong proficiency in SQL and experience with modern data transformation tools (such as dbt or equivalent)
• Experience designing and maintaining data models for business and analytics use
• Familiarity with data pipeline orchestration and cloud data warehouse platforms
• Strong communication skills for working with both technical and non-technical stakeholders