Data Engineer

Job Description – Data EngineerEmployment 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 OverviewWe 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 Must-Have Skills • Apache Spark (PySpark and/or Scala Spark) • Apache Iceberg • Informatica Data Integration • MinIO 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