- Define and implement the overall data architecture strategy, leveraging Microsoft Fabric to its fullest potential.
- Design and oversee the development of highly scalable and efficient data architectures using Microsoft Fabric components (Data Factory, Synapse Data Engineering, Dataflows Gen2, Lakehouse).
- Establish robust data models, schemas, and data integration patterns to support complex analytical and reporting requirements.
- Develop and enforce comprehensive data governance policies and standards, ensuring data quality, security, and compliance.
- Serve as the subject matter expert on Microsoft Fabric, providing expert guidance and mentorship to the data engineering team who currently have limited fabric experience.
- Lead the evaluation and adoption of new Fabric features and capabilities, ensuring the team stays at the forefront of technology.
- Develop and deliver comprehensive training and knowledge transfer sessions on Fabric best practices and advanced techniques.
- Define and implement strategic data integration strategies for diverse data sources, including ERP systems, databases, APIs, and flat files.
- Design and oversee the implementation of efficient ETL/ELT processes using Fabric components and other relevant technologies.
- Develop complex data transformation logic to support advanced business intelligence and analytics initiatives.
- Collaborate closely with data analysts and business stakeholders to define data requirements and develop effective data solutions.
- Design and implement data architecture solutions that are inherently scalable, performant, and reliable.
- Provide strong technical leadership and expert guidance to the data engineering team.
- Mentor and train team members on core data architecture principles and Microsoft Fabric best practices.
- 5+ years of proven experience as a Data Architect, with a strong focus on Microsoft technologies.
- Demonstrable knowledge in Microsoft Fabric, including Data Factory, Synapse Data Engineering, Dataflows Gen2, and Lakehouse.
- Strong, in-depth understanding of data warehousing, data modeling, and data integration principles.
- Proficiency in SQL, Spark (Scala, Python, or SQL), and other relevant technologies.
- Demonstrated ability to design and implement highly scalable and performant data architectures.
- Strong leadership and mentoring capabilities.