Data Engineer

Datamatics-Global-Services-Ltd; Dammam; On-site; SAR 240,000 - 420,000; Full time

Job summary

Datamatics-Global-Services-Ltd seeks a Data Engineer to design, build, and optimize data ingestion and transformation workloads on a modern on-prem lakehouse platform. You will work with Spark-based development and Informatica-based integration solutions to enable analytics and data consumption.

The role emphasizes data modeling with Iceberg tables, governance, and scalable pipelines. Experience with Denodo, Starburst, or Dremio is a plus, and on-prem data environments demand strong

Qualifications

• Proficiency with Spark (PySpark/Scala) and Iceberg data models.
• Informatica Data Integration for enterprise data pipelines.
• Experience with MinIO/object storage and data lake concepts.
• Experience with Denodo, Starburst and/or Dremio is a plus.
• Strong SQL, data modeling, and data warehousing concepts.
• ETL/ELT development and version control with Git.
• Hands-on data pipeline development and governance practices.
• Python and streaming technologies are advantageous.

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.

Skills

Apache Spark

Apache Iceberg

Informatica Data Integration

MinIO Storage

Denodo

SQL & Data Modeling

Data Warehousing

ETL/ELT

Git Version Control

Python

Kafka

Tools

Starburst

Dremio

Python

Kafka

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