Data Engineer

• Demonstrable experience with dbt Core or dbt Cloud in production environments (2+ years).

• Solid knowledge of Azure Data Factory: pipelines, triggers, linked services, Integration Runtimes.

• Advanced SQL proficiency and experience with Snowflake or similar cloud platforms (BigQuery, Redshift).

• Familiarity with data architecture patterns: Medallion (Bronze/Silver/Gold), Data Vault, dimensional modeling.

• Experience with Git and CI/CD workflows for data projects.

• Knowledge of Python for data engineering tasks (PySpark, Pandas, automation scripts).

• Experience with data governance platforms or catalogs, and with dbt Semantic Layers.

• Familiarity with Power BI or modern BI tools as consumers of data layers.

• Knowledge of AI/ML tools in the data context (MLflow, Feature Stores, etc.).

• Experience with Ask Your Data-type tools.

The Data Engineer role is eminently technical and execution-focused. We are looking for someone who enjoys writing code, building robust pipelines, and quickly solving the business's changing needs. The Data Engineer will be the technical owner of Azure Data Factory (ADF) and dbt - the tools that bring the Snowflake Data Lake to life - and will be the piece that allows the team to move fast without breaking anything. Their work is measured in real deliveries: ETLs that work, models that reflect the business, and data that arrives clean and on time to those who need it.

• Be the technical reference for dbt on the team: model design, Staging / Intermediate / Mart layers, quality tests, and documentation.

• Develop and maintain the Data Lake's Silver and Gold models in coordination with data domain owners.

• Establish and enforce development conventions (naming, lineage, testing, materialization).

• Integrate dbt with existing data observability and governance tools.

• Contribute to the Data Platform architecture and roadmap.

• Be the technical owner of ADF pipelines: design, deployment, monitoring, and incident resolution.

• Manage ingestion from current source systems (ERP, CRM, Marketing tools, etc.) into the Bronze/Silver layer of Snowflake.

• Optimize the performance and cost of existing pipelines and design new ones as the scope grows.

• Apply a Data Governance first mindset: naming, lineage, classification, and traceability are part of the job, not a later step.

• Work side by side with BI analysts, marketing automation, and the AI Taskforce to ensure data arrives on time and in good condition.

• Act as the technical counterpart to the external platform provider, ensuring the coherence and quality of deliverables.

• Contribute to a data quality culture: automated tests, documentation, lineage, and identifying issues for handling with data stewards.

• Keep the data catalog up to date and support the Governance team in the evolution of assets.

• Maintain and evolve the current security framework.

• Apply security and data masking policies to ensure privacy and proper use.

• Ownership mindset: able to take a technical domain from start to finish, with autonomy, integrity, and a sense of responsibility.

• Service spirit: understands that their work exists so others can make better decisions. Data is the means; people are the end.

• Excellence and continuous improvement: proposes standards, documents, and does not leave technical debt unmanaged.

• Clear communication with non-technical profiles: knows how to translate engineering decisions into business language.

• Teamwork in small, agile environments with direct exposure to management.

• Adaptability to an environment under construction: no fear of a blank page or making decisions with incomplete information.