Azure Data Lakehouse Lead & Enterprise Data Architect

Azure Data Lakehouse Lead & Enterprise Data Architect

Full-Time 70000 - 90000 Β£ / year (est.) No working from home possible
McGregor Recruitment

At a Glance

  • Tasks: Lead the transformation of data architecture and design a modern Azure-based platform.
  • Company: Join McGregor Boyall, a forward-thinking company in London.
  • Benefits: Enjoy a hybrid work model with competitive salary and career growth.
  • Other info: Collaborate with diverse teams in a dynamic and innovative environment.
  • Why this job: Shape the future of data and AI while leading a talented team.
  • Qualifications: Experience in data architecture and leadership skills required.

The predicted salary is between 70000 - 90000 Β£ per year.

McGregor Boyall is recruiting an Enterprise Data Architect in London for a hybrid role, leading the transformation across data, AI and enterprise architecture. You will own the data architecture strategy and design a modern Azure-based data platform, guiding data products, governance and AI capabilities across the business.

The role includes:

  • Line management of two Data Architects
  • Collaboration with Architecture, Engineering, Data and AI teams
  • Ensuring scalable, secure, AI-ready data

Azure Data Lakehouse Lead & Enterprise Data Architect employer: McGregor Recruitment

Join a leading global professional services firm that prioritises innovation and employee development in the rapidly evolving field of AI. With a hybrid work environment, you will benefit from a collaborative culture that encourages creativity and offers ample opportunities for growth and advancement. Experience the unique advantage of working with cutting-edge technology on impactful projects that deliver real enterprise value.

McGregor Recruitment

Contact Details:

McGregor Recruitment Recruitment Team

We think you need these skills to ace Azure Data Lakehouse Lead & Enterprise Data Architect

Communication Skills
Python
SQL
Problem-Solving Skills
Data Engineering
Data Governance
Data Pipeline Development