About this role

Develop data pipelines for ingestion, transformation and delivery at scale;
Work with modern architectures such as Lakehouse, Data Mesh and Medallion;
Ensure data quality, traceability, versioning and safe reprocessing of data;
Support the implementation of DataOps practices, monitoring and observability;
Build solutions focused on performance, governance and security;
Collaborate with architects, data scientists and data analysts;
Participate in technical discussions with clients, recommending best practices and efficient solutions.

Experience with ETL/ELT for data collection, transformation and loading;
Experience with cloud and on-premises data architectures (Azure and/or Google Cloud);
Monitoring and optimization of data pipelines;
Strategies for data quality, validation and integrity;
Implementation of Data Lakes and Data Warehouses;
Process automation using Python and SQL;
Knowledge in:
Advanced Python and SQL
Relational and NoSQL databases (SQL Server, PostgreSQL, MongoDB, etc.)
Azure Data Factory, Synapse, Databricks, Fabric or similar
Version control with Git
Concepts of DataOps and Data Governance
Clear, structured technical documentation.

Mid-level Data Engineer

Key skills