Data Engineer

Datamatics Technologies; Al Jubayl; On-site; SAR 170,000 - 300,000; Full time

Job summary

Datamatics Technologies is seeking a Data Engineer to build and optimize data ingestion, transformation, and consumption workloads on a modern on-premises lakehouse platform. The role emphasizes Spark-based development, Iceberg tables, and Informatica-based integration.

You will collaborate with Platform Engineers to deploy, test, and operationalize data pipelines, while ensuring data quality and governance across enterprise environments.

Qualifications

• Hands-on Spark development experience (PySpark/Scala).
• Experience with Apache Iceberg and data lakehouse concepts.
• Proficiency in Informatica Data Integration for enterprise workloads.
• Experience with MinIO Object Storage for data assets.
• Familiarity with Denodo for data virtualization and analytics.
• Strong SQL skills and data modeling for warehousing.
• Understanding of data warehousing concepts and ETL/ELT.
• Version control using Git in data pipelines and deployments.

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 Iceberg tables.
• Implement and support data integration workflows using Informatica.
• Enable analytics through Denodo, Starburst, and/or Dremio.
• Develop secure, governed, and scalable data pipelines.
• Support batch and near real-time data processing.
• Implement data quality, metadata management, and validation controls.
• Collaborate with Platform Engineers on deployment and integration.
• Support migration, testing, and production rollout initiatives.
• Troubleshoot and optimize pipeline performance and scalability.

Skills

Spark (PySpark/Scala)

Apache Iceberg

Informatica Data Integration

MinIO Object Storage

Denodo

SQL & Data Modeling

Data Warehousing Concepts

ETL/ELT Development

Git Version Control

Tools

Starburst

Dremio

Data Governance

Data Quality Frameworks

CI/CD for Data Pipelines

Python

Kafka

Lakehouse Architecture Patterns

Job description

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