Lead Data Architect in London

The Role

• Provide overall technology leadership for the data transformation program, with accountability for data architecture, Data Mesh implementation, and the end-to-end delivery of data products.

• Work with client leadership, domain teams, architects, analysts, modelers, platform teams, and engineers to translate business priorities into scalable data products and reusable enterprise capabilities.

Your responsibilities

• Define and govern the overall data architecture, Data Mesh implementation approach, and data product delivery principles for the program.

• Act as the overall program technology lead, ensuring alignment across business priorities, domain architecture, data modelling, platform capabilities, engineering, governance, and consumption requirements.

• Work with different levels of client management to support strategy definition, delivery planning, implementation oversight, architectural decision-making, and frictionless delivery of data products.

• Collaborate with domain consultants and business stakeholders to identify, define, decompose, and prioritize data products.

• Establish architecture standards and decision frameworks for data product boundaries, domain ownership, interoperability, sharing, discoverability, quality, security, and lifecycle management.

• Lead the design of reusable frameworks, foundational capabilities, templates, and components that accelerate the end-to-end data product lifecycle.

• Lead proofs of concept, proofs of technology, architecture assessments, and evaluation exercises, and present findings and recommendations to client stakeholders.

• Work with engineering teams to ensure optimal data product design, including ingestion, transformation, storage, orchestration, quality, security, observability, and consumption patterns.

• Review solution designs, resolve cross-domain architecture concerns, manage technical dependencies, and govern architecture exceptions.

• Ensure alignment between data product delivery and enterprise data governance, metadata, lineage, access control, data quality, and certification requirements.

• Provide technical direction to architects, data modelers, analysts, and engineers, and facilitate architecture and design reviews across delivery teams.

• Identify architectural risks and delivery constraints, define mitigation actions, and communicate technology decisions and implications to program leadership.

• Drive consistency and reuse across domains while allowing appropriate autonomy for domain- specific implementation decisions.

Essential skills/knowledge/experience:

• Strong experience leading enterprise data architecture and large-scale data transformation programs.

• Deep understanding of Data Mesh, data products, domain-driven design, federated governance, data product lifecycle management, and data-as-a-product principles.

• Experience defining enterprise, domain, conceptual, logical, and physical data architectures.

• Strong understanding of end-to-end data lifecycle management.

• Experience designing reusable data platform capabilities, engineering frameworks, architecture patterns, and delivery accelerators.

• Ability to translate business strategy and domain requirements into pragmatic architecture and executable delivery plans.

• Experience leading proofs of concept, technology evaluations, architecture reviews, and executive-level presentations.

• Strong understanding of modern data platforms like databricks

• Proven ability to lead multidisciplinary teams involving domain experts, business analysts, data modelers, platform architects, governance specialists, and data engineers.

• Strong stakeholder management, facilitation, technical leadership, communication, and decision-making skills.

Preferred Qualifications

• Experience implementing Data Mesh or domain-oriented data product operating models in a large enterprise.

• Experience working in regulated industries.

• Knowledge of Databricks, cloud data platforms (AWS), semantic layers, data catalogues, and modern data governance solutions.

• Experience establishing architecture governance, design authorities, reusable knowledge assets, and data product standards.

• Relevant AWS Cloud, data architecture, enterprise architecture, or Databricks certification.

Nice to Have

• Databricks Certified Data Engineer Associate/Professional.

• Experience working with globally distributed teams and enterprise-scale data platforms.

• Exposure to AI/ML workloads on Databricks.

Desirable skills/knowledge/experience:

• Experience in Commodity Trading, Energy Trading, or Supply Chain domains.

• Exposure to real-time data processing and event-driven architectures.

• Knowledge of Power BI, Tableau, or other analytical reporting platforms.

• Understanding of Data Mesh architecture and data product thinking.

• Databricks certification is preferred.

Primary Technologies:

Databricks, PySpark, Delta Lake, Unity Catalog, Delta Live Tables, SQL, Azure , Git, CI/CD, Data Modeling.