Senior Analytics Engineer - Data Modeling

Job Snapshot

• Role: Senior Analytics Engineer - Data Modeling
• Location: Dubai, United Arab Emirates
• Industry: Internet
• Function: Database Development-Administration
• Experience: Strong experience in analytics engineering, data modeling, SQL, Python, dbt, cloud data platforms
• Job Type: Full-time

Job Details

• Country: United Arab Emirates
• City: Dubai
• Industry: Internet
• Function: Database Development-Administration
• Salary: 25000-42000 (estimated)
• Gender: Any
• Candidate Nationality: Any
• Job Type: Full-time

Overview

Senior Analytics Engineer - Data Modeling in Dubai is an opportunity for a technically strong data professional who can transform raw information into reliable, scalable, and business‑ready data models for Talabat. This role supports smarter decision‑making across product, operations, commercial, finance, and customer experience teams by building clean SQL and dbt models, improving data quality, and supporting self‑service analytics.

Key Responsibilities

• Design, build, and maintain clean, efficient, and scalable data models using SQL, dbt, and modern analytics engineering practices.
• Transform raw data into structured datasets that support business intelligence, self‑service analytics, reporting, and advanced analysis.
• Optimize SQL queries, transformation workflows, and data processing logic to improve speed, cost efficiency, and reliability.
• Work closely with data engineers to define data requirements and strengthen ETL or ELT pipelines across cloud‑based data platforms.
• Partner with product analysts, data scientists, business teams, and operational stakeholders to ensure datasets answer real reporting and decision‑making needs.
• Develop and maintain data transformation workflows using dbt, SQL, Python, and cloud warehouse environments.
• Implement data quality rules, automated checks, validation routines, and reconciliation processes to improve accuracy and consistency.
• Monitor data integrity and investigate data issues before they affect dashboards, reporting, or business decisions.
• Troubleshoot pipeline failures, model errors, freshness delays, and data discrepancies with a structured and proactive approach.
• Build data models that are easy to understand, reusable, well‑documented, and aligned with agreed business definitions.
• Support the development of semantic layers, reporting‑ready tables, and standardized metrics for cross‑functional analytics.
• Automate data validation, reporting support processes, and recurring data checks where possible.
• Advocate for strong documentation, testing, version control, peer review, and maintainable analytics workflows.
• Use Git and CI‑CD practices to support reliable deployment, controlled changes, and collaborative data development.
• Contribute to performance improvement, data governance, and model design standards across analytics engineering workstreams.
• Help teams move from manual reporting and fragmented data use toward trusted, scalable, and reusable data products.

Ideal Profile

• Bachelor's degree in Engineering, Computer Science, Technology, Data, or a similar field.
• Postgraduate qualification in a relevant field is a plus but not required.
• Strong proficiency in SQL and Python for data transformation, modeling, automation, and analytical workflows.
• Hands‑on experience with dbt for data modeling, testing, documentation, and transformation workflow management.
• Experience working with cloud‑based data warehouses such as Snowflake, BigQuery, Google Cloud Platform, Redshift, or similar platforms.
• Good understanding of data engineering principles, including ETL, ELT, data pipelines, data lineage, and warehouse design.
• Familiarity with Git, version control, code review, and CI‑CD practices for data workflows.
• Strong ability to translate business requirements into reliable data models, reporting tables, and reusable analytical datasets.
• Practical experience improving data quality, query performance, model reliability, and analytics documentation.
• Comfortable working with analysts, data scientists, data engineers, and business stakeholders in a fast‑paced technology environment.
• Strong problem‑solving skills with the ability to investigate data issues, identify root causes, and implement durable fixes.
• Detail‑oriented working style with a clear focus on accuracy, consistency, scalability, and maintainability.
• Able to communicate technical concepts clearly to both technical and non‑technical stakeholders.
• Interested in building intelligent data foundations that improve customer, partner, rider, and business decision‑making.

Skills Set

• Analytics engineering
• Data modeling
• SQL
• Python
• dbt
• Data transformation
• ETL
• ELT
• Cloud data warehouses
• Snowflake
• BigQuery
• Google Cloud Platform
• Redshift
• Data pipeline support
• Query optimization
• Data quality testing
• Automated validation
• Data integrity monitoring
• Business intelligence support
• Self‑service analytics
• Reporting datasets
• Semantic data models
• Data documentation
• Version control
• Git
• CI‑CD
• Data troubleshooting
• Data governance support
• Performance optimization
• Stakeholder collaboration
• Product analytics support
• Data science enablement