Principal Data Architect (Azure)

About the role

Sparq is seeking a Principal Data Architect to lead the design and evolution of enterprise data platforms on Azure. You will shape lakehouse, data‑warehouse, pipeline, and semantic layers to ensure scalability, performance, and alignment with corporate standards while leveraging AI tools to enhance productivity.

Key responsibilities

• Design and evolve enterprise data architecture across lakehouse, data‑warehouse, pipeline, and semantic layers.
• Translate high‑level strategy into actionable technical plans, breaking work into epics, features, and sprint‑ready stories.
• Lead technical design discussions, identify dependencies, risks, and trade‑offs.
• Establish and enforce best practices for data modeling, pipeline design, data quality, and taxonomy.
• Review architecture and code for complex data solutions to ensure consistency and optimal performance.
• Mentor engineers and provide architectural guidance across Data Engineering, Data Science, and Enterprise Architecture.
• Design and build scalable data pipelines and semantic models using Microsoft Fabric and Azure services.
• Support migration from on‑prem SQL Server to cloud‑based platforms and optimize workflows for cost and reliability.
• Partner with Data Scientists to productionize machine‑learning models within enterprise pipelines.
• Create and maintain clear technical documentation covering architecture, standards, and key systems.

Required profile

• Deep expertise in modern data architecture, especially on Azure and Microsoft Fabric.
• Proven experience designing large‑scale lakehouse and data‑warehouse solutions.
• Strong ability to translate strategy into detailed implementation plans.
• Excellent communication skills to bridge engineering, data science, and enterprise teams.
• Track record of mentoring technical staff and driving best‑practice adoption.

Required skills

• Azure
• Microsoft Fabric
• SQL Server (on‑prem and cloud)
• Data lakehouse and data‑warehouse design
• Data pipelines and orchestration
• Semantic modeling
• Data modeling and taxonomy
• Data quality management
• AI/ML tooling and model operationalization
• Enterprise architecture principles

What we offer

• Collaborative, diverse team environment.
• Continuous learning through training, mentorship, and certifications.
• Hands‑on work with cutting‑edge technologies across industries.
• Recognition programs and AI Innovators Program.
• Remote or hybrid work options.