Data Engineer

techcarrot is looking for a Data Engineer to join the team onsite in Dubai. This full-time role centers on building data solutions with the Microsoft and Databricks stack, turning raw data into insights that support core intelligence products.

The role suits someone who enjoys collaborating across squads, turning progress updates into clear, actionable reports, and shaping data features from early design through implementation. If you’re comfortable presenting complex data insights to non-technical stakeholders and taking ownership of end-to-end data workflows, this could be a great match. The company is a technology-focused outfit working on intelligence products, and this position is based in Dubai, onsite.

You’ll contribute as a team player, coordinating with multiple engineering groups to help bring proven prototypes into production-ready data capabilities, while keeping communications clear and purposeful across the organisation.

Building scalable data pipelines for core intelligence products

On a typical day you’ll collect updates from the Squad and turn them into a concise weekly status report for Squad Leads to review. You’ll join early state design discussions to help shape features and data requirements, then take charge of implementing reliable data pipelines using the Microsoft and Databricks stack. You’ll focus on building reusable, scalable data flows that support core intelligence offerings, and you’ll develop and manage L3-level project plans with detailed tasks, milestones, and dependencies. Collaboration with other engineering teams is a constant, ensuring that prototypes evolve into robust, production-ready solutions and that insights reach stakeholders in a clear, accessible way.

This role blends hands-on engineering with planning and coordination. You’ll be expected to translate complex data findings into practical recommendations, while aligning the work with broader project timelines and cross-team dependencies. The day-to-day mix is about building, refining, and communicating data products that drive real decisions across the organisation.

What you need to bring to the table

• Experience working with both relational and non-relational databases.
• Strong analytical capabilities for handling both structured and unstructured data.
• Hands-on use of Azure Databricks with Spark to design and run ETL pipelines.
• Solid skills in data analysis, manipulation, and modeling using Spark, Python, Scala, SQL or similar tools.
• Proven ability to build JIRA dashboards that reflect tasks and progress.
• Azure DevOps experience to deploy data pipelines through CI/CD.

Nice-to-haves

• Project management experience, coordinating milestones and dependencies across teams, is a plus.

Qualifications and experience you’ll bring

• Minimum 5-7 years of practical experience.
• Bachelor’s degree in computer science, software engineering, information technology, or a related field.
• In-production experience with the Azure cloud stack.

Pay, benefits and practical details

Job type: full-time. Work arrangement: onsite. Location: Dubai, AE.

Advice for applicants

Lead your resume with your 5-7 years of hands-on data engineering work, especially projects that used Azure Databricks and Spark to build ETL pipelines. Mention concrete outcomes, like how pipelines improved data availability or insight delivery, and highlight experience deploying pipelines with CI/CD in Azure DevOps.

Demonstrate the must-have skills by sharing concrete examples of databases you’ve worked with (both relational and non-relational), how you handled structured and unstructured data, and how you built dashboards in JIRA to reflect task progress. If you’ve led data projects across squads, show the end-to-end flow from design to production and the impact on the product team.

Prepare to discuss end-to-end data pipelines in interviews: how you gather requirements, design with early-stage features in mind, implement scalable data flows, and validate results. Be ready to explain how you manage tasks, milestones, and dependencies at the L3 level, and how you keep stakeholders informed with clear data storytelling.

Ask a targeted question to close: how does the data engineering team collaborate with other squads to integrate prototypes into the core intelligence products, and what metrics define success for these pipelines in production?