Data Engineer (ETL, Data Pipelines & Warehousing)
Amman, Jordan Data Engineer (ETL, Data Pipelines & Warehousing)
About the job Data Engineer (ETL, Data Pipelines & Warehousing)
Aumet is a leading healthcare technology company dedicated to revolutionizing the way medical supplies are sourced and distributed globally. Our platform connects healthcare providers with a vast network of suppliers, streamlining the procurement process and ensuring efficient access to essential medical products.
Job Overview:
The Data Engineer is responsible for building and managing scalable data pipelines, ETL processes, and data warehouses. This role ensures that data is efficiently ingested, transformed, and made available for analytics, reporting, and AI model training, while ensuring data quality and security.
Responsibilities:
Design and maintain efficient and scalable
data pipelines for structured and unstructured data. Implement and optimize
ETL processes to ingest and transform data for various modules. Manage relational and NoSQL databases, such as
PostgreSQL for transactional data and
MongoDB for unstructured data. Build and optimize
data warehouses (e.g.,
Amazon Redshift, Google BigQuery) to support reporting and advanced analytics. Collaborate with Data Scientists to ensure data pipelines support the needs of AI models. Manage
real-time data streaming using tools like
Apache Kafka or
RabbitMQ. Monitor and troubleshoot data pipelines to ensure data quality, performance, and scalability. Ensure data security, including
encryption at rest and in transit.
Requirements:
Minimum 4 years of experience Strong experience in
SQL and
NoSQL databases (e.g., PostgreSQL, MongoDB). Expertise in designing and managing
data pipelines and
ETL processes. Experience with
data warehousing technologies such as
Amazon Redshift, Google BigQuery. Familiarity with
real-time data processing tools like
Kafka, RabbitMQ. Solid understanding of
data security, including encryption and compliance protocols. Experience with
large-scale data environments and optimizing queries for performance. Proficiency with
data integration between different systems. Experience working with
data scientists to support AI and machine learning models.* Strong problem-solving skills and ability to manage complex data systems.
