Data Engineer
Summary
Data Engineer in Saudi Arabia who designs, builds, and optimizes Spark-based ETL/ELT pipelines on an on-premises lakehouse platform. Core stack includes Apache Spark, Apache Iceberg, MinIO, Informatica, and Denodo/Starburst/Dremio for analytics enablement, with 5-10 years of experience required.
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.
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