Build, maintain and evolve scalable, resilient data pipelines (ETL/ELT);
Drive the evolution of the data platform architecture;
Develop solutions using Databricks, AWS, Python and Spark;
Implement and manage data orchestration processes;
Ensure data quality, observability, traceability and availability;
Apply DataOps practices (CI/CD, testing, versioning and monitoring);
Support cost management through FinOps practices;
Contribute to initiatives around data-as-a-product and data monetization;
Support governance and the evolution of the data catalog;
Serve as a technical reference and provide technical leadership within the team;
Collaborate with Data Science, Product and Engineering teams;
Work with agile methodologies.

Bachelor's degree in IT or a related field.
Postgraduate degree in IT or a related field preferred.
Solid experience in Data Engineering and DataOps practices;
Experience with Databricks, AWS, Python, Spark, GitHub (version control and code review);
Experience with agile methodologies;
Strong background in data platform architecture.
Desired:
Experience with data catalogs and governance;
Knowledge of FinOps;
Experience with data-as-a-product and data monetization;
Experience with Data Science (for integration and support);
Experience in the financial market or regulated environments.

Senior Data Engineer

Key skills