Experteer Overview
Por favor, asegúrese de leer completamente el resumen y los requisitos de esta oportunidad de empleo que se detallan a continuación.
As Lead Data Engineer, you design and maintain robust data pipelines across the procurement domain, aligning with DevOps practices in Azure DevOps and GitHub. You own end-to-end data products from ingestion to the Gold layer, champion data quality and governance, and mentor cross-functional teams to deliver scalable, production-grade solutions. You will work with dbt, Databricks, PySpark, and CI/CD automation to enable reliable, observable data platforms. This role offers impact at scale and opportunities to shape data engineering standards in a global, science-driven environment.
Compensaciones / Ventajas
• Design and implement end-to-end data pipelines across Bronze, Silver, and Gold layers
• Develop data ingestion from source systems via ETL and API methods
• Build modular transformation frameworks with dbt, PySpark, SQL; create staging models and load metadata aligning with Data Vault 2.0
• Design and maintain CI/CD and DevOps processes using Azure DevOps and GitHub Actions
• Implement IaC and deployment automation for data platform resources (dbt jobs, Databricks workflows, clusters)
• Establish monitoring, observability, and operational excellence with proactive maintenance and incident resolution




• Drive data quality, governance, and FAIR data principles across all platform layers
• Provide technical leadership, collaborate with data engineers, modelers, stewards, BI developers, data scientists and business SMEs
Responsabilidades
• 5+ years of production-grade data pipeline experience
• Strong dbt, SQL, Python, Spark (PySpark), Databricks, Git, Azure DevOps, and GitHub experience
• Deep CI/CD and automation experience with Azure DevOps Pipelines and GitHub Actions
• IaC knowledge (Terraform, Bicep) considered a strong advantage
• Cloud data engineering expertise (Azure) with Databricks jobs, clusters, xqbhyrx and workflows in production
• Extensive knowledge of Data Vault 2.0 architecture (Raw Vault, Business Vault, Gold)
• Ingestion and integration patterns (ETL, incremental processing, CDC, API-based ingestion)
• Data governance/quality experience with FAIR principles and data discoverability
• Leadership and cross-functional collaboration skills
Requisitos principales
• Unique career paths across health, nutrition, and beauty
• Impact millions of consumers daily
• Growth and leadership development
• Collaborative culture
• Voice matters and inclusion
• Learning and mobility across businesses

📌 Lead Data Engineer (Cross Domain) (Barcelona)
🏢 Dsm
📍 Barcelona