Data Engineer

We are seeking a skilled Data Engineer to join our team in Saudi Arabia on a full-time basis. The ideal candidate will have between 5 to 10 years of hands-on experience in Spark development, Informatica integration solutions, and working with enterprise data platforms. This role focuses on building, configuring, and optimizing data ingestion, transformation, and consumption workflows within a modern on-premises lakehouse environment. The successful candidate will demonstrate strong expertise in Spark-based development, enterprise data lake or lakehouse implementations, and Informatica-driven data integration.

Key Responsibilities

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

Required Qualifications

- Proven experience with Apache Spark development (PySpark and/or Scala Spark).
- Strong knowledge of Apache Iceberg for data modeling and management.
- Hands-on expertise in Informatica Data Integration solutions.
- Familiarity with MinIO object storage systems.
- Experience working with Denodo or similar data virtualization platforms.
- Proficiency in SQL and data modeling techniques.
- Solid understanding of data warehousing concepts and architecture.
- Expertise in ETL/ELT development and best practices.
- Experience using Git for version control in collaborative environments.

Preferred Qualifications

- Experience with Starburst and/or Dremio platforms.
- Knowledge of data governance frameworks and metadata management practices.
- Familiarity with data quality frameworks and validation methodologies.
- Understanding of CI/CD processes tailored for data pipelines.
- Programming skills in Python.
- Experience with Kafka or other streaming technologies.
- Exposure to lakehouse architecture patterns and enterprise data lake implementations.
- Ability to perform performance tuning and optimization of large-scale data workloads.

This position offers the opportunity to work on cutting-edge data engineering projects within a dynamic enterprise environment. Candidates with a strong technical background and a passion for building efficient, scalable data solutions are encouraged to apply.