Data Engineer

Datamatics Technologies LLC

Al Jubayl

Presencial

SAR 240.000 - 360.000

Jornada completa

Hace 2 días
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Descripción de la vacante

Datamatics Technologies LLC is seeking a Data Engineer to design, build, and optimize data ingestion, transformation, and consumption workloads on a modern on-prem lakehouse platform. Candidates should have hands-on Spark development, Iceberg, and Informatica experience.

You will implement secure, governed data pipelines, enable analytics with Denodo, Starburst, or Dremio, and collaborate with Platform Engineers on deployment and testing across batch and near real-time processing.

Formación

  • 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

Responsabilidades

  • 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.

Conocimientos

Apache Spark
Apache Iceberg
Informatica Data Integration
MinIO Object Storage
Denodo
SQL and Data Modeling
Data Warehousing Concepts
ETL/ELT Development
Git Version Control

Herramientas

Git

Descripción del empleo

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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