What is the day to day like for an Ituber in this area?

• Design, build, and continuously evolve scalable, resilient, and high‑performance data pipelines, supporting a wide range of analytical, operational, and data science use cases.
• Promote the democratization of data access, ensuring datasets are reliable, well documented, properly governed, and easily consumable by analysts, data scientists, and business teams.
• Develop ETL/ELT pipelines using Python, SQL, and Spark/PySpark, processing large volumes of data in distributed environments.
• Implement and maintain data solutions on AWS, leveraging services such as S3, Glue, EMR, Lambda, Redshift, Athena, Kinesis, and MWAA (Airflow), with a strong focus on efficiency, cost optimization, and scalability.
• Define and apply analytical data models (Data Warehouse, Data Lakehouse), ensuring adherence to best practices for performance, usability, and data consumption.

Required Technical Skills

• Advanced / Fluent English – Mandatory
• Strong proficiency in Python for building data pipelines, automation, and data processing workflows.
• Advanced knowledge of SQL, including complex queries, window functions, CTEs, and performance optimization.
• Hands-on experience with Apache Spark / PySpark for large-scale, distributed data processing.
• Solid experience designing and implementing ETL/ELT pipelines in cloud-based and distributed environments.
• Proven experience with AWS data services, including:

Storage & Data Lake: Amazon S3, AWS Lake Formation

Processing: AW