• 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