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Datamatics Technologies

Data Engineer

Datamatics Technologies Dammam, Eastern Province, Saudi Arabia

IT Services and IT Consulting · 51-200 employees

Oct 15
data-engineer Senior (5-10 yrs) Full-time Saudi Arabia
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About the role

Design and develop scalable data ingestion and transformation pipelines while maintaining Spark-based ETL/ELT workloads. Collaborate with platform engineers to ensure secure, governed, and optimized data consumption across the enterprise lakehouse.

What they look for

Apache Spark PySpark Scala Spark Apache Iceberg Informatica Data Integration MinIO Denodo SQL Data Modeling Data Warehousing ETL/ELT Git Starburst Dremio Python

Requirements

Requires hands-on experience with Spark development, Informatica integration, and enterprise data lake implementations. Proficiency in SQL, data modeling, and modern data platform tools like Denodo or Starburst is essential.

Full 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

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