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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Architect Consultant - **Company:** Transilvania HR - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Business Intelligence Development, BigQuery, Information Systems, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Migration, Data Systems, Data Warehousing, Meta-Data Management, Reference Data, SQL Databases, Data Streaming, Cloud Platform System, Snowflake, Event Driven Architecture, Information Technology, Data Lineage, Data Analytics, Apache Kafka, Spark Streaming, Data Management, Physical Data Models, Virtual Agents, Azure Synapse Analytics, Databricks - **Published:** May 23, 2026 - **Apply:** https://www.opcionempleo.com/jobad/es0e134d6fa74ff63501683a1418d91b94 ## About the Role * Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related field. * 5+ years of experience in data architecture or a senior data role with architecture responsibilities. * Demonstrable experience delivering data architecture solutions in complex, multi-source enterprise environments. Technical Skills * Strong data modelling skills across conceptual, logical, and physical layers. * Proficiency in SQL and working knowledge of data warehousing and lakehouse principles. * Experience designing data architectures on at least one major cloud platform (Snowflake, Databricks, Azure Synapse, BigQuery, etc.). * Familiarity with data integration patterns and ETL/ELT tooling. * Working knowledge of metadata management, data cataloguing, and data lineage tools. * Familiarity with data mesh or domain-oriented architecture principles. Nice to Have * Experience with Master Data Management (MDM) platforms and methodologies. * Exposure to real-time or event-driven architecture (Kafka, Spark Streaming, or similar). * Knowledge of AI/ML data requirements and how to architect platforms that support model development and inference. * Experience with Snowflake Intelligence or similar agentic data platform features. Soft Skills * Ability to turn complex data realities into clear architecture decisions. * Strong facilitation and stakeholder communication skills, including executive-level audiences. * Structured, documentation-oriented mindset with attention to accuracy and detail. * Curiosity and adaptability in a fast-moving data and AI landscape. * Upper-intermediate English proficiency. ## Description HazelHeartwood works on medium to large scale Digital, Data, and AI transformation projects across domains such as Marketing, Sales and Logistics. With proven expertise in Master Data Management, Data Analytics, Customer and Employee Engagement, Agentic AI, and Business Transformation Orchestration, we help clients dream realistically; defining a vision, plan and implement that dream through roadmap design and execution. We lead the market in the automotive and service industry and serve clients across all sectors. We are a team that came together from all over the world: creative, curious and always challenging the status quo. We use technology to make a direct business impact - improving our clients' bottom line and long-term sustainability., HazelHeartwood is looking for a Data Architect Consultant with strong experience in designing and implementing scalable, enterprise-level data solutions. In this role, you will translate client realities into relevant data architectures - from conceptual models to physical implementations - enabling analytics, AI, and operational excellence across our client engagements. We are looking for an architect who combine classical architecture discipline with a modern understanding of data platforms designed for analytics and AI workloads., Data Architecture Design * Design end-to-end data architectures tailored to client business context, maturity level, and technology landscape. * Develop conceptual, logical, and physical data models that support reporting, analytics, and AI use cases. * Define data architecture patterns (e.g. lakehouse and data mesh) and guide their implementation. * Assess existing data landscapes and produce gap analyses, architecture blueprints, and migration roadmaps. * Ensure architectural decisions balance scalability, cost, performance, and maintainability. Data Modeling & Governance * Design and maintain master and reference data models in alignment with business domains. * Establish data modelling standards, naming conventions, and documentation practices across projects. * Collaborate with Data Governance leads to ensure models support lineage, quality, and compliance requirements. * Define entity relationships, hierarchies, and business rules in collaboration with domain stakeholders. Client Engagement & Solutioning * Engage directly with clients to understand their data reality: sources, volumes, structures, and business objectives. * Facilitate architecture workshops and actively participate in discovery sessions to surface requirements and constraints. * Produce and present architecture artefacts (diagrams, data flow maps, decision logs, etc) to both technical and non-technical audiences. Platform & Integration * Design integration architectures connecting source systems, data platforms, and consumption layers. * Work across modern cloud data platforms such as Snowflake, Databricks, and Azure. * Define ingestion patterns appropriate to client data volumes and latency needs. * Support the selection and configuration of data platform components: storage, compute, orchestration, and cataloguing tools. Collaboration & Communication * Work closely with Data Engineers, BI Developers, AI Engineers, and Data Translators to ensure architectural alignment across delivery. * Partner with client stakeholders across business and IT to build shared understanding of architecture decisions and foster long-term alignment. * Communicate trade-offs and recommendations clearly to different stakeholders, adapting depth and framing without losing technical accuracy. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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