Data Engineers

• Design, build and maintain data processing ETL pipelines & data products in a multi-cloud, multi-region, distributed processing context choosing the right technologies.
• Drive data investigations to deliver a resolution of technical, procedural, and operational issues. 
• Solve complex business problems by utilizing disciplined development methodology, producing scalable, flexible, efficient and supportable solutions using appropriate technologies.

Skills: 
Data engineering patterns: Sound knowledge of the different data engineering patterns to determine what to use and when 
Data architecture: Understanding of data architecture and frameworks like Data mesh, data fabric etc. 
Big Data Technologies: Proficiency in big data technologies such as data lake, EMR, Glue for analysing large datasets. 
Programming Languages: Proficiency in programming languages commonly used in data engineering, such as Python, Java, or Scala. with OOP expertise 
Data Warehousing: Strong knowledge of data warehousing solutions preferably Amazon Snowflake. 
ETL/ELT Tools: Familiarity with orchestration tools 
Database Systems: Expertise in both relational and NoSQL databases. 
Data Modelling: Skill in designing efficient data models, both for OLAP and OLTP systems. 
Streaming Data: Knowledge of streaming data technologies. 
Version Control: Experience with version control systems : Git. 
Containerization and Orchestration: Understanding of containerization technologies (Docker) and container orchestration platforms (Kubernetes). 
Data Security: Knowledge of data encryption, access control, and compliance with data privacy regulations. 
Monitoring and Logging: Proficiency in setting up monitoring and logging solutions for data pipelines using different tools