Data Engineer

We are seeking an experienced Data Engineer to join our team in Saudi Arabia. The successful candidate will be responsible for building, configuring, and optimizing data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform. This role requires strong hands-on experience with Spark development, enterprise data lake/lakehouse implementations, and Informatica-based integration solutions. The ideal candidate will have between 5 to 10 years of relevant experience and a solid understanding of scalable data pipeline architectures.

Key Responsibilities

- Design and develop scalable data ingestion and transformation pipelines to support enterprise data needs.
- Build, optimize, and maintain Spark-based ETL/ELT workloads using PySpark and/or Scala Spark.
- Develop and manage data models and datasets utilizing Apache Iceberg tables.
- Implement and support data integration workflows leveraging Informatica Data Integration tools.
- Enable analytics and data consumption through platforms such as Denodo, Starburst, and/or Dremio.
- Develop secure, governed, and scalable data pipelines aligned with enterprise standards and compliance requirements.
- Support batch and near real-time data processing workflows to meet business demands.
- Implement data quality, metadata management, and validation controls to ensure data integrity.
- Collaborate closely with Platform Engineers on deployment, integration, and operationalization activities.
- Assist in migration, testing, and production rollout initiatives to ensure smooth transitions.
- Troubleshoot and optimize data pipeline performance, reliability, and scalability for large-scale workloads.

Required Qualifications

- Proven expertise in Apache Spark development (PySpark and/or Scala).
- Hands-on experience with Apache Iceberg for data modeling and management.
- Strong knowledge of Informatica Data Integration solutions.
- Familiarity with MinIO Object Storage systems.
- Experience with data virtualization tools such as Denodo.
- Proficiency in SQL and data modeling techniques.
- Solid understanding of data warehousing concepts and ETL/ELT development.
- Experience using Git for version control and collaborative development.

Preferred Qualifications

- Experience with Starburst and/or Dremio for data analytics and query acceleration.
- Knowledge of data governance frameworks and metadata management practices.
- Familiarity with data quality frameworks and implementation strategies.
- Understanding of CI/CD pipelines for data engineering workflows.
- Programming skills in Python.
- Exposure to Kafka or other streaming technologies for real-time data processing.
- Deep understanding of lakehouse architecture patterns and enterprise data lake implementations.
- Proven ability to tune and optimize performance of large-scale data workloads.

This role offers the opportunity to work on cutting-edge data platforms within a dynamic environment, contributing to the enterprise’s data strategy and analytics capabilities. Candidates with a passion for data engineering and a strong technical background are encouraged to apply.