Arquitecto de Datos

About the role

We are looking for a Data Architect to lead the design, development, evolution and operation of data platforms and products built on Microsoft Fabric. The role will act as the technical reference for the team, defining architecture standards, promoting DataOps, quality and governance, and mentoring other engineers. The opportunity is open to candidates located in Costa Rica or El Salvador.

Key responsibilities

• Design end‑to‑end data architectures on Microsoft Fabric aligned with business, security and scalability needs.
• Lead development of pipelines using Azure Data Factory, Dataflows Gen2, notebooks and APIs.
• Design and evolve solutions in OneLake, Lakehouse, Warehouse, Eventhouse and real‑time intelligence.
• Implement Medallion architecture (Bronze, Silver, Gold) and define standards for each layer.
• Create ingestion, transformation, incremental load, SCD, deduplication and audit processes.
• Develop and optimise solutions with SQL, Python, PySpark, Spark and Delta Lake.
• Integrate Lakehouse, Warehouse, semantic models and Power BI.
• Define standards for quality, observability, security, lineage and disaster recovery.
• Optimise performance, capacity, cost and reliability of solutions.
• Lead resolution of complex incidents and define preventive actions.
• Mentor Data Engineers and Data QA, conduct code reviews and promote best practices.
• Drive Git, Azure DevOps, CI/CD and DataOps to automate development and deployment cycles.
• Collaborate with Architecture, Cybersecurity, Data Governance, BI, AI and business areas.

Required profile

• University degree in Computer Science, Engineering, Data Science or a related field.
• Proven experience leading data‑engineering initiatives and platforms.
• Hands‑on experience designing and implementing enterprise solutions on Microsoft Fabric and the Azure ecosystem.

Required skills

• Microsoft Fabric
• Azure Data Factory
• Dataflows Gen2
• OneLake, Lakehouse, Warehouse, Eventhouse
• Medallion architecture
• SQL
• Python
• PySpark / Spark
• Delta Lake
• Power BI
• Git
• Azure DevOps
• CI/CD pipelines
• DataOps practices