Data Engineering

Goal of Position

Data Engineering covers the collection, modeling, storage, processing and evaluation of extremely large (e.g. social media), rapidly changing (e.g. sensor data) and complex (e.g. ecological systems) data sets. The data sets may origin and/or concern all information processes in the business. The term complex refers to the massive, interrelated, and dynamic nature of data sets to be analyzed. 

Your Qualifications

• Graduate Degree in Computer Science or Information Systems, Applied Statistics or Mathematics, Software Engineering.
• 3 to 5 years of applicable working experience
• Strong programming skills in Python, preferably with experience in pySpark
• Expertise in SQL and relational database technologies (e.g., SQL Server), including developing complex queries and query optimization; experience with NoSQL databases is a plus.
• Extensive experience designing, building, and optimizing large-scale, cloud-based data architectures and pipelines using cloud platforms such as GCP, AWS, or Azure (preferred).
• Proficiency in ETL/ELT design, development, and automation in batch and real-time environments.
• Experience in data modeling and performance optimization to support analytics, reporting, and machine learning needs is preferred.
• Knowledge of CI/CD pipelines, unit testing practices, and Kubernetes is desirable.
• Deep understanding of data governance, data lake house architectures, security, and compliance, including policy implementation for data lineage, retention, and access control.
• Proven ability to collaborate cross-functionally with stakeholders and mentorship experience in agile, data-driven environments.
   

Your Key Responsibilities

• Develop, manage, optimize and automate data pipelines and integration solutions for global operations, enabling collection, storage, processing, and analysis of complex data sets using big data tools, ETL processes, real-time data processing, API development, and data virtualization.
• Design, build, and operate scalable, cloud-based data architectures and applications, ensuring data reliability, performance, and accessibility across functions and businesses. 
• Define and enforce data governance policies, including data ingestion, retention, lineage, access, and API management, in collaboration with data management and IT teams, focusing on digital platforms and data lakes.
• Collaborate with stakeholders to gather requirements, deliver milestones, and enable user self-service data transformation and visualization (e.g., MS Power BI).
• Partner with IT and analytics teams to develop end-to-end custom data science solutions within an agile framework.