Databricks architect

• Design end-to-end data architectures using Databricks for analytics, data engineering, and data science use cases.
• Lead and support data platform migrations into Databricks from legacy or alternative data platforms.
• Define architectural standards, best practices, and reference patterns for Databricks implementations.
• Collaborate with data engineers, platform teams, and stakeholders to translate business requirements into scalable technical solutions.
• Ensure solutions meet performance, security, scalability, and cost-optimisation requirements.
• Provide technical leadership and architectural governance across Databricks initiatives.
• Review existing data architectures and recommend improvements or modernisation strategies.
• Support teams with architectural guidance, troubleshooting, and design reviews.

The ideal candidate will have several years' experience operating at architect level, with proven success designing and/or migrating data platforms into Databricks. Candidates with strong architecture backgrounds on comparable platforms (such as Snowflake) will also be considered, provided they hold relevant Databricks certifications., * Proven experience working as a Data Architect / Platform Architect at a senior level.

• Hands-on experience designing solutions within the Databricks ecosystem OR

• Strong architectural experience on a competing platform (e.g. Snowflake) combined with Databricks certifications.

• Demonstrated experience with data platform migrations, modernisation, or large-scale data transformations.
• Strong understanding of data architecture principles, including:

• Data lakes / lakehouse architectures
• Data modelling and data integration patterns
• Performance optimisation and scalability

• Experience working with cloud-based data platforms.
• Strong stakeholder communication and documentation skills.

Desirable Skills & Experience

• Databricks certifications (Data Engineer, Data Architect, or equivalent).
• Experience designing solutions prior to Databricks (traditional data warehouses, big data platforms, or cloud-native data stacks).
• Knowledge of modern data engineering tools and frameworks.
• Experience operating in complex enterprise environments