Data Engineering

We are seeking a talented and driven Data Engineer to build and optimize our data pipelines, data warehouses, and processing systems. You will work closely with data scientists, analysts, and other stakeholders to ensure the integrity, scalability, and efficiency of our data architecture. Your expertise will be crucial in enabling the seamless flow of data across various systems and platforms, laying the groundwork for data-driven decisions at all levels of the organization.

Key Responsibilities:

Design, develop, and maintain scalable and efficient data pipelines and ETL processes for large datasets.
Collaborate with data analysts and data scientists to understand data needs and ensure the availability of clean, reliable, and high-quality data.
Build and maintain data warehouses, ensuring the optimization of storage and retrieval processes.
Monitor and optimize the performance of data systems, ensuring fast and efficient data access and analysis.
Implement data integration strategies to support various business applications and analytics tools.
Troubleshoot data issues and work to improve overall system performance and reliability.
Ensure the security and compliance of all data systems and processes.
Stay current on emerging data technologies and best practices to continuously improve infrastructure.
Qualifications:

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field.
Proven experience as a Data Engineer or in a similar technical role.
Strong programming skills in languages such as Python, Java, Scala, or similar.
Proficiency with SQL and experience working with relational databases (e.g., MySQL, PostgreSQL).
Hands-on experience with data warehousing technologies (e.g., Redshift, BigQuery, Snowflake).
Experience with data pipeline orchestration tools (e.g., Apache Airflow, Luigi, or similar).
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and distributed computing (e.g., Spark, Hadoop).
Experience with version control systems (e.g., Git) and CI/CD pipelines.
Strong understanding of data modeling, data governance, and data architecture principles.
Excellent problem-solving skills and the ability to work with large, complex datasets.
Preferred Qualifications:

Knowledge of containerization tools like Docker and orchestration platforms like Kubernetes.
Familiarity with machine learning pipelines and integration with big data platforms.
Experience with stream processing technologies (e.g., Kafka, Kinesis).
Certifications in cloud technologies or data engineering platforms (e.g., AWS, Google Cloud).

Please send your CV.