Data Engineer

Role overview

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 processing; implement data quality, metadata management, and validation controls.
• Collaborate with Platform Engineers on deployment and operationalization; support migration, testing, and production rollout.
• 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, 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, and enterprise data lake/lakehouse implementation experience.
• Performance tuning and optimization of large-scale data workloads.

Work location

• The description specifies work location as KSA.

Requirements

• 5–10 years of experience is specified.
• Hands-on experience with Spark development, Informatica integration solutions, and enterprise data platforms.
• Experience with Apache Spark, Apache Iceberg, Informatica Data Integration, MinIO Object Storage, and Denodo.
• Experience with SQL, data modeling, data warehousing concepts, ETL/ELT development, and Git version control.
• Starburst and/or Dremio experience is advantageous.
• Data governance, metadata management, data quality frameworks, or CI/CD for data pipelines are advantageous.
• Python, Kafka or streaming technologies, and lakehouse architecture experience are advantageous.
• Enterprise data lake/lakehouse implementation and large-scale data workload optimization experience are advantageous.