Data Engineer

About the Role
You will build and manage the pipelines that transport data from operational systems into the warehouse, as well as the data models that everything downstream relies on. This role encompasses ingestion, processing, and modeling, along with the necessary documentation. You will also conduct the analytical work that ensures the integrity of the data.

What You Will Do

• Profile new data sources before building: databases, APIs, files, open data, both structured and unstructured.
• Build, schedule, and monitor ingestion and processing pipelines using the current tools, with the flexibility to extend them where appropriate.
• Design and construct warehouse models (star schemas, conformed dimensions) that serve as the foundation for dashboards and machine learning use cases.
• Implement data-quality checks and lineage capture within every pipeline, and maintain current documentation for conceptual, logical, and physical models.
• Collaborate with business owners to reconcile figures, define KPIs, and assist in composing the monthly progress report.

What You Need

• Strong proficiency in Python and SQL, applied in production environments.
• Experience with Spark (Databricks or Hadoop) and BigQuery, with a solid understanding of both data-lake and warehouse patterns.
• Practical knowledge of at least two of the following tools: Talend, Informatica, NiFi, and Denodo.
• Experience in extracting and capturing changes from MS SQL Server, Oracle, MySQL, and PostgreSQL.
• Skills in ingesting data from APIs, files (SFTP, Excel, CSV), and open data, across batch and streaming processes.
• Expertise in dimensional modeling, including star schemas, conformed dimensions, and slowly changing dimensions, along with the ability to thoroughly document models at conceptual, logical, and physical levels.
• Understanding of orchestration, monitoring, retries, data-quality checks, and lineage.
• Experience in profiling, reconciling data, and defining KPIs in collaboration with business users.

Good to Have

• Familiarity with dbt, Airflow, Dataflow, and Iceberg.
• Certifications such as Google Professional Data Engineer or Databricks.