Data Engineer - Datamatics Technologies

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.

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