Hybrid Data Engineer: Build Scalable Pipelines & Data Platforms

Hybrid work: 3 days in the office and 2 days remote per week

A Data Engineer is responsible for implementing, building, testing, and maintaining the infrastructure and architecture required for the efficient and reliable processing of various types of data. Selected candidate works with Data Analysts, Data Scientists, and other stakeholders to understand the data requirements, identify data sources, and implement the necessary processes to transform and load the data into the target systems.

They are also responsible for ensuring the integrity, availability and security of the data throughout its lifecycle, and for continuously optimizing the data pipelines to meet the evolving business needs.

• Strong understanding of data engineering concepts, including data warehousing, ETL/ELT processes and data governance
• Experience in planning and maintaining data lakes and pipeline processes, both batch-wise and (near) real-time
• Experience in using of data engineering tools to support development of data science, data analytics and AI applications for both, structured and unstructured, data
• Experience with monitoring and logging tools to ensure the reliability and performance of data pipelines and AI systems
• Solid understanding of containerization and orchestration tools such as Docker and Kubernetes
• Experience with cloud platforms such as AWS, Google Cloud or Azure
• Solid understanding of good software development principles such as version control, CI/CD, unit/functional testing
• Strong proficiency in Python for building and integrating various data engineering components
• Solid understanding of data security best practices, including data encryption, access controls, authentication/authorization mechanisms, and compliance requirement