Role & seniority: Data Automation Engineer (entry to mid level; ~1+ years’ experience)

Design, develop, and maintain automated data pipelines for ingesting, processing, and storing structured and unstructured data (real-time and batch)
Implement data quality checks, monitoring, and issue remediation; ensure data accuracy, consistency, and reliability
Collaborate with data teams and stakeholders; document architectures, processes, and code; support data security/compliance and optimization initiatives

1+ years in data engineering, automation, or related field
Strong understanding of data pipelines and data lifecycle management
Proficiency in scripting (Python, Shell) and at least one major cloud platform (AWS/Azure/GCP)
Knowledge of data warehousing and data quality methodologies
Analytical, problem-solving abilities; ability to collaborate across teams

Location & work type: EG-Banha, Egypt (EI Corniche Street); Full-time, Information Technology

Full Description

As a Data Automation Engineer, you will play a crucial role in developing and maintaining automated data processing systems. You will work closely with data analysts, and other stakeholders to design, implement, and optimize data pipelines, ensuring the efficient and accurate flow of data throughout the organization.

Develop and implement automated data pipelines for ingesting, processing, and storing structured and unstructured data.
Integrate data from various sources in both real-time and batch mechanisms and perform transformations to ensure consistency, accuracy, and reliability.
Implement data quality checks and monitoring mechanisms to identify and address issues proactively.
Write code and scripts for automation of data processes using languages such as Python, Java, or Scala.
Leverage ETL (Extract, Transform, Load) tools and frameworks [Apache spark, AWS Glue] for efficient data processing.
Collaborate with other intelligent data teams.
Ensure seamless data flow for machine learning and analytics applications.
Implement monitoring systems to track data pipeline performance and identify areas for optimization.
Continuously optimize data processes for efficiency and scalability.
Document data processes, system architecture, and codebase for knowledge sharing and future reference.
Keep documentation up-to-date with any changes or enhancements to the data infrastructure.
Implement and adhere to data security and compliance standards.
Collaborate with security teams to ensure data protection and privacy.