> Markdown version of [/jobs/ext/4714325-databricks-solution-architect](https://www.wearedevelopers.com/jobs/ext/4714325-databricks-solution-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Databricks Solution Architect - **Company:** Experis - **Location:** London, United Kingdom - **Salary:** £182,000 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Engineering, Data Architecture, Information Engineering, Data Governance, Data Warehousing, Meta-Data Management, Data Streaming, Google Cloud, Data Lakes, Data Management, Machine Learning Operations, Data Pipelines, Databricks - **Published:** November 14, 2026 - **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-6AFCBCBA9D5DA0372E/ ## About the Role * Proven expertise with Databricks including: * Databricks Lakehouse Architecture * Delta Lake * Unity Catalog * Databricks Workflows * Databricks SQL * MLflow * Data Governance and Security Strong cloud architecture experience within: * Microsoft Azure (preferred) * AWS or Google Cloud Platform Deep understanding of: * Data warehousing * Data lake architectures * Master Data Management (MDM) * Data modelling and metadata management * Data integration patterns Experience designing enterprise-scale analytics and Business Intelligence platforms. Strong stakeholder management and communication skills. Experience leading multi-disciplinary technical teams ## Description * Architect and optimise solutions leveraging Databricks Lakehouse Platform. * Design scalable data pipelines supporting batch, streaming, analytics, and AI workloads. * Provide architectural governance, standards, and best practices. * Collaborate with senior stakeholders, programme teams, data engineers, analysts, and business leaders. * Ensure solutions meet security, compliance, data governance, and regulatory requirements. * Support migration of Legacy data platforms to modern cloud-based environments. * Drive adoption of modern data engineering and analytics patterns. * Produce architecture artefacts including HLDs, LLDs, reference architectures, and technical governance documentation.