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
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