Build and manage the semantic layer: Create the logical data layer, ensuring business rules are centralized and consistent across the company.
Governance and organization: Act as the guardian of data quality and standardization, structuring data so it is easily accessible before reaching analysts.
Requirements extraction: Interface directly with stakeholders to translate business needs into efficient dimensional models.
AI automation: Implement and promote the use of AI tools (such as Claude) to optimize development, documentation, and data processing.
Technical evolution: Maintain and evolve orchestration processes, unit tests, and versioning, applying engineering best practices to the analytics context.

Advanced SQL: Proficiency in complex transformations and query optimization.
Data modeling: Strong experience in dimensional modeling and analytical environments.
Hands-on experience with programming languages (preferably Python), orchestration systems (such as Airflow or Dagster), and version control tools (Git).
Experience with Google Cloud Platform (BigQuery): Specific knowledge in table optimization and processing in BigQuery.
Data Warehousing: Experience with Data Warehouse or Data Lakehouse architectures, focusing on cost optimization and performance.
Critical thinking in AI: Familiarity with and openness to using Generative AI in the workflow.
CI/CD and quality: Experience with unit testing and continuous integration pipelines.

Analytics Engineer – Affirmative-Action Position for People with Disabilities (PWD)

Key skills