Data Engineer

We have partnered with a high-growth technology business in the UAE to hire a Data Engineer. This is a business where data infrastructure is taken seriously and the people building it are given the tools, autonomy and scope to do it properly. If you are five years into your data engineering career and looking for a role where you can level up technically while working on problems that actually matter, this is worth your attention.
This role suits an engineer who cares as much about reliability and data quality as they do about throughput, and who wants to work in an environment that shares those values.
About the role:

• Design, build and maintain scalable data pipelines that move, transform and serve data across the business in both batch and streaming environments
• Own the development and continuous improvement of the data platform, ensuring reliability, performance and scalability as the business grows
• Work closely with data scientists, analysts and AI engineers to ensure clean, well-structured data is available where and when it is needed
• Implement data quality frameworks, testing practices and observability tooling that keep pipelines trustworthy in production
• Contribute to data modelling and architecture decisions, building infrastructure that scales with the ambitions of the business
• Support the AI and ML agenda by designing data infrastructure that feeds model training, feature engineering and inference pipelines

About you:

• 5 years of experience in data engineering or a closely related discipline

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• Strong proficiency in Python and SQL with a solid understanding of software engineering principles applied to data
• Hands-on experience with cloud data platforms such as Snowflake, BigQuery or Redshift
• Experience building and maintaining both batch and streaming pipelines using frameworks such as Apache Spark, Kafka or Airflow
• Familiarity with dbt or equivalent transformation tooling and modern data stack practices
• Solid understanding of data governance, quality and security considerations in a production environment