Role Description Enterprise AI is forcing organisations to rethink their data estates. Data platforms designed mainly for reporting are often not enough for GenAI, semantic search, agentic workflows and AI-enabled decision-making. Clients now need data that is trusted, governed, contextualised and consumable by both people and intelligent systems. We are looking for client-facing Enterprise Data Architects to join our growing Enterprise AI practice. You will help clients transform fragmented data estates into AI-ready foundations, advising on architecture decisions across: - Cloud data platforms - Lakehouse and warehouse patterns - Data products - Semantic layers - Metadata - Lineage - Governance - Knowledge graphs - GenAI retrieval patterns This is a consulting role, not a purely internal architecture role. You will: - Diagnose ambiguous client problems - Shape options and make trade-offs explicit - Translate complex data architecture issues into clear decisions for both technical teams and executive stakeholders You will work in cross-functional teams alongside: - Product owners - Data scientists - ML and GenAI engineers - Data engineers - Business analysts - Client stakeholders Typical outputs may include: - Target-state architectures - Maturity assessments - Platform option appraisals - Data product designs - Governance models - Lineage maps - Ontology and semantic models - Integration patterns - GenAI data-readiness assessments - Implementation roadmaps We are hiring across several levels. At earlier levels, we expect: - Strong architecture delivery experience - Hands-on platform understanding At senior levels, we expect: - The ability to shape enterprise data strategy - Influence senior stakeholders - Lead complex architecture decisions - Guide multi-disciplinary delivery teams We do not expect every candidate to be a specialist in every aspect of AI-ready data architecture. We are looking for architects with: - Strong core data architecture experience - Credible depth in some areas that matter for AI-enabled data estates, such as governance, semantic modelling, lakehouse architecture, data products, metadata management, knowledge graphs, RAG or enterprise data strategy Design AI-ready enterprise data architectures that enable analytics, AI, ML, GenAI and agentic applications to consume data accurately, securely and with appropriate business context. - Assess clients’ existing data estates - Diagnose structural, governance, semantic and quality issues - Design pragmatic modernisation roadmaps - Advise clients on architecture and platform choices - Define data governance and metadata patterns - Design data products, data contracts and information models - Shape semantic layers, ontologies and knowledge graph patterns - Oversee high-level design of ingestion, integration and transformation patterns - Identify and mitigate data-related risks - Act as a trusted advisor to client stakeholders - Contribute to proposals, client conversations, internal methods and thought leadership Qualifications - 5–10+ years, depending on level, in data architecture, enterprise architecture, solution architecture or senior data engineering roles - Demonstrable experience designing modern data architectures for analytics, AI, ML or GenAI consumption - Strong understanding of enterprise data architecture patterns - Experience contributing to or leading data governance initiatives - Practical understanding of semantic layers, ontologies or knowledge graph concepts - Deep experience with at least one major cloud data platform - Understanding of how data architecture decisions affect AI and GenAI outcomes - Familiarity with GenAI data patterns - Strong stakeholder management and communication skills - Excellent written and verbal communication skills in English - Bachelor’s degree or equivalent experience; quantitative, technical or analytical disciplines are an advantage - Willingness to travel, up to around 60% depending on project requirements Requirements - A second major European language is an advantage - Experience with graph modelling, ontology standards or graph query languages - Familiarity with feature store design and MLOps / DataOps pipeline integration - Experience with stream processing at scale using Apache Kafka or Apache Flink - Background in master data management or data mesh architecture - Consulting or comparable client-facing delivery experience - Exposure to some relevant technologies Benefits - Industry-leading compensation and benefits - Top training and development opportunities

• Build and maintain Snowflake data pipelines for Dealer 360 and Aftermarket workstreams respectively • Design and implement the dealer and aftermarket feature stores (Layer 1–2) • Build ingestion pipelines for all external data sources (JD Power, PIN, S&P, Vehicle Registration, competitive scraping + 2 TBC) • Write and maintain dbt models for data transformation, cleaning, and normalisation • Enforce schema validation, data quality checks, and freshness SLAs across all feeds • Collaborate with the Data Architect to implement the unified data model • Produce documented data lineage for every pipeline before any model is trained against it

• Soporte y mantenimiento de un data warehouse en SQL Server con pipelines complejos • Liderazgo técnico en tareas de re-ingeniería y modernización de la arquitectura de datos • Garantizar la continuidad operativa durante procesos de migración o reestructuración • Colaborar con el equipo interno para planificar y ejecutar cambios de infraestructura • Desarrollo y mantenimiento de pipelines con Azure Data Factory • Generación de reportes y dashboards en Power BI para distintos stakeholders

• Lead and develop a diverse, distributed team of platform and data engineers • Ensure security, stability, and scalability are foundational properties of the platform • Co-own the multi-year platform engineering roadmap • Lead the evolution of Keyloop’s shared services, API gateway, integration layer • Establish API-first as a non-negotiable architectural principle • Drive cloud-native architecture decisions • Champion platform security-by-design • Architect and evolve the integration platform • Lead the design and delivery of Keyloop's data platform • Drive strategic adoption of technologies including Snowflake, Databricks, AWS Athena • Lead Keyloop's transition to an agentic software development lifecycle • Oversee the execution of platform engineering initiatives • Collaborate closely with the CTO, engineering directors, architects, and product leadership