Data Engineer

Job description

Job Description - Data Engineer Employment Type: Full-Time Work Location: KSA Experience Required: Hands-on experience with Spark development, Informatica integration solutions, and enterprise data platforms. Years of experience: 5 to 10 Role Overview We are looking for a Data Engineer to build, configure, and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform. The ideal candidate will have strong hands-on experience with Spark-based development, enterprise data lake/lakehouse implementations, and Informatica-based integration solutions. Key Responsibilities • Design and develop scalable data ingestion and transformation pipelines. • Build, optimize, and maintain Spark-based ETL/ELT workloads. • Develop and manage data models and datasets using Apache Iceberg tables. • Implement and support data integration workflows using Informatica. • Enable analytics and data consumption through Denodo, Starburst, and/or Dremio. • Develop secure, governed, and scalable data pipelines aligned with enterprise standards. • Support batch and near real-time data processing requirements. • Implement data quality, metadata management, and validation controls. • Collaborate with Platform Engineers on deployment, integration, and operationalization activities. • Support migration, testing, and production rollout initiatives. • Troubleshoot and optimize data pipeline performance, reliability, and scalability. About this profession: A Data Engineer is the professional responsible for designing, building, and maintaining data architectures that power business analytics and AI. They collect data from diverse sources, transform it into usable formats using ETL tools and cloud platforms, and ensure data quality, security, and scalability to meet organizational needs. Typical duties of this profession: • Design, build, and maintain data pipelines and data flows • Develop ETL processes to integrate data from diverse sources • Ensure the quality, security, and reliability of data infrastructure • Collaborate with data analysts and data scientists to support analytics needs

Requirements

• Apache Spark (PySpark and/or Scala Spark)
• Apache Iceberg
• Informatica Data Integration
• the employer Object Storage
• Denodo
• SQL and Data Modeling
• Data Warehousing Concepts
• ETL/ELT Development
• Git Version Control
• Good-to-Have Skills
• Starburst and/or Dremio
• Data Governance and Metadata Management
• Data Quality Frameworks
• CI/CD for Data Pipelines
• Python
• Kafka or Streaming Technologies
• Lakehouse Architecture Patterns
• Experience with enterprise data lake and lakehouse implementations
• Performance tuning and optimization of large-scale data workloads

Skills