Job Title: Data Engineering & Warehousing Engineer
Experience: 3–11 Years
Location: Riyadh - Onsite
Employment Type: Full-Time
We are seeking a highly skilled Data Engineering & Warehousing Engineer with 3–11 years of experience to design, develop, and maintain scalable data platforms and enterprise data warehouse solutions. The ideal candidate will have hands-on expertise in building ETL/ELT pipelines, data integration, cloud-based data platforms, and big data processing technologies. You will play a key role in enabling reliable, high-performance analytics and business intelligence solutions.
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Design, develop, and optimize scalable ETL/ELT pipelines for structured and unstructured data.
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Build and maintain enterprise data warehouses, data lakes, and modern data platforms.
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Develop real-time and batch data processing solutions.
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Integrate data from multiple internal and external sources while ensuring data quality and governance.
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Collaborate with Data Scientists, BI Developers, and business stakeholders to support analytical requirements.
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Optimize data storage, query performance, and pipeline reliability.
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Implement data security, monitoring, and governance best practices.
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Troubleshoot and resolve data pipeline and platform issues.
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Participate in architecture discussions and contribute to data platform modernization initiatives.
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Hands-on experience with Google BigQuery and Dataflow and Dataproc and Pub/Sub.
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Experience with Azure Synapse and Azure Data Factory.
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Experience with Amazon Redshift and AWS Glue.
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Strong experience with Apache Spark and Apache Kafka.
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Experience building batch and real-time data processing pipelines.
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Hands-on experience with dbt or Oracle Data Integrator (ODI) for data transformation and orchestration.
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Experience implementing ETL/ELT best practices and reusable data models.
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Strong experience with Oracle or PostgreSQL.
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Expertise in SQL, relational database design, performance tuning, and query optimization.
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Experience with data modeling, data governance, metadata management, and data quality frameworks.
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Knowledge of dimensional modeling and modern data warehouse architectures.
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Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field.
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3–11 years of professional experience in Data Engineering, Data Warehousing, or Big Data technologies.
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Strong programming and scripting skills using SQL, Python, or similar languages.
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Excellent analytical and problem-solving abilities.
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Experience working in Agile/Scrum development environments.
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Experience with cloud-native data lake and lakehouse architectures.
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Knowledge of CI/CD pipelines and Infrastructure as Code (IaC).
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Familiarity with containerization technologies such as Docker and Kubernetes.
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Experience supporting machine learning and analytics workloads.
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Cloud certifications on AWS, Microsoft Azure, or Google Cloud Platform are a plus.
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Google Cloud Data Services: BigQuery and Dataflow and Dataproc and Pub/Sub
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Azure Data Services: Azure Synapse and Azure Data Factory
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AWS Data Services: Amazon Redshift and AWS Glue
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Data Processing: Apache Spark and Apache Kafka
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Data Transformation: dbt or Oracle Data Integrator (ODI)
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Databases: Oracle or PostgreSQL
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Programming: SQL and Python
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Cloud Platforms: Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred)
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