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Generalist data role, broad multi-cloud/big-data skillset and metro location increase candidate competition.
Core data engineering skills (ETL, SQL, Spark, cloud) are broadly transferable across industries.
Explicit 3–11 years requirement plus comprehensive multi-cloud and big-data tech stack narrows suitable candidates.
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Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data.
Build and maintain enterprise data warehouses, data lakes, and modern data platforms across multiple cloud environments (Google Cloud, Azure, AWS).
Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical and business intelligence requirements while ensuring data quality, governance, and security.
3–11 years of professional experience in Data Engineering, Data Warehousing, or Big Data technologies.
Bachelor's degree in Computer Science, IT, Data Engineering, Software Engineering, or related field.
Hands-on experience with cloud data platforms including Google BigQuery, Azure Synapse, and AWS Redshift plus ETL/ELT tools like dbt or Oracle Data Integrator.
Located in Riyadh with onsite work requirement.
Experienced in building and managing batch and real-time data processing pipelines using Apache Spark and Kafka.
Skilled in scalable cloud data platform architecture involving multiple cloud providers (Google Cloud, Azure, AWS).
Capable of driving data platform modernization with strong expertise in data modeling, governance, metadata management, and performance tuning.