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Data Engineer

Datamatics Global Services Ltd
Riyadh, KSA
Full-time
Onsite
Discovered 1 weeks ago
Apache Spark (PySpark and/or Scala Spark)Apache IcebergInformatica Data IntegrationMinIO Object StorageDenodoSQL

Full Job Posting

Role overview

Build, configure, and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform.

The role requires hands-on Spark-based development, enterprise data lake/lakehouse implementations, and Informatica-based integration solutions.

Employment type: Full-time. Work location stated as KSA. Experience required: 5–10 years.

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, and Denodo.
  • SQL and data modeling, data warehousing concepts, ETL/ELT development, and 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 and performance tuning and optimization of large-scale data workloads.

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