Missions
Design, develop, and maintain robust and scalable data pipelines (batch and/or streaming).
Ensure the ingestion, transformation, and availability of data from multiple sources to data warehouses / data lakes.
Supervise and ensure the reliability of data processing (monitoring, alerting, incident recovery).
Identify, analyze, and resolve incidents related to data flows (performance, quality, availability).
Optimize the performance of ETL/ELT processing and SQL queries.
Automate data processes via Python scripts (orchestration, quality controls, cleaning).
Participate in data modeling (schemas, tables, analytical views).
Contribute to the industrialization of data flows (testing, CI/CD, documentation).
Work in collaboration with BI teams to populate reporting and visualization tools.
Participate in projects for migration, modernization, or redesign of data platforms.
Maintain a technological watch on Data Engineering, Cloud, and DataOps tools.
Profile
Education & Experience
Master's degree in computer science, data engineering, information systems, or equivalent.
Proven experience (minimum 4 to 5 years) as a Data Engineer or in an equivalent role.
Technical Skills
Excellent proficiency in Python.
Solid experience with ETL / ELT tools and orchestration (Airflow, Talend, dbt, or equivalent).
Strong mastery of relational databases (SQL Server, PostgreSQL…) and advanced SQL.
Experience with Data Warehouse / Data Lake environments.
Knowledge of at least one BI visualization tool (Power BI, Tableau, Qlik, Looker…).
Proficiency in versioning and CI/CD tools: Git, GitLab, Jenkins.
Knowledge of DataOps best practices (monitoring, testing, data quality).
Soft Skills
Analytical mindset and rigor
Autonomy and sense of responsibility
Ability to work in a distributed environment
Good interpersonal skills and ability to collaborate with technical and business profiles
Performance and data quality orientation
