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Data Engineer

Datamatics Technologies · Riyadh

Posted Oct 16, 2026Source: naukrigulf
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Job description

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. 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 Share

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