Lead Data Architect

Position Summary Acuity is seeking a Senior Data Architect to join the Acuity Digital Center of Excellence (DCoE) in Cork and define and guide scalable, governed data solutions across Databricks, Azure Data Lake, and the enterprise lakehouse. This is a hands-on architecture role that translates business and analytics needs into practical data architectures, reusable patterns, and technical roadmaps. The role partners closely with data engineering, platform, reporting, analytics, AI, and business teams, including supply chain and manufacturing stakeholders. This role combines deep technical leadership with hands-on architecture and coding, solution design, and engineering governance. The Senior Data Architect partners with Product Management, Engineering, Enterprise Architecture, DevOps, Security, and other business stakeholders to ensure solutions are scalable, secure, maintainable, and aligned to business objectives. Key Responsibilities Architecture & Strategy - Define target-state and transition architectures for Databricks, Azure Data Lake, and enterprise lakehouse capabilities. - Create reference architectures and standards for data ingestion, ETL/ELT, modelling, integration, security, governance, and consumption. - Translate business capabilities and non-functional requirements into logical and physical data architecture decisions. - Lead architecture and design reviews, document decisions, and maintain a prioritized architecture roadmap. - AI Fluency & Emerging Technologies: Pragmatically evaluate and incorporate artificial intelligence, machine learning pipelines, and LLM integrations into core system architectures to modernize capabilities and improve operational efficiency. Data Modelling & Governance - Define domain-oriented data models, bronze/silver/gold patterns, data contracts, and reusable data-product standards. - Establish expectations for metadata, cataloguing, lineage, data quality, retention, privacy, and role-based access. - Align curated data with reporting, Power BI, analytics, data science, and AI use cases. - Partner with governance and security teams to ensure solutions follow enterprise standards. Delivery Leadership - Provide hands-on technical direction, prototypes, and design support throughout Agile delivery. - Guide engineering teams on scalability, reliability, performance, cost optimization, and maintainability. - Identify architecture risks, dependencies, and technical debt and track decisions and actions through Azure DevOps. - Mentor engineers and promote practical architecture guardrails without creating unnecessary delivery friction. Required Qualifications - Bachelor’s degree in computer science, information systems, engineering, or a related field, or equivalent practical experience. - 10+ years of hands-on experience in data architecture, data engineering, or solution architecture for modern enterprise data platforms. - Strong practical expertise with Databricks, Azure Data Lake, Delta Lake, and lakehouse architecture. - Strong knowledge of ETL/ELT patterns, dimensional and domain modelling, batch and streaming integration, and data-quality controls. - Experience designing data platforms that support enterprise reporting, analytics, and self-service consumption. - Working knowledge of Agile delivery, Git, CI/CD, DevOps practices, and Azure DevOps. - Experience working within Agile and SAFe delivery models. - Excellent communication, stakeholder management, and technical leadership skills. Preferred Qualifications - Proficiency with AI-assisted software development practices. - Databricks or Microsoft Azure certification. - Experience in supply chain, manufacturing, ERP, operations, or industrial data domains. - Experience with Unity Catalog, metadata-driven frameworks, Power BI semantic models, AI/ML data readiness, or real-time data architectures. - Experience leading architecture reviews, technical strategy, and portfolio-level technology decisions. Soft Skills - Strong analytical and structured problem-solving skills. - Clear communication across technical and business audiences. - A collaborative, ownership-focused approach to delivery. - Ability to balance quality, speed, maintainability, and operational needs. - Ability to explain architecture trade-offs and influence decisions without direct authority. Tools & Technologies - Architecture: Databricks, Azure Data Lake, Delta Lake, enterprise lakehouse. - Development: Multi-language fluency. - Data design: domain and dimensional modelling, bronze/silver/gold, data contracts. - Governance: Unity Catalog, metadata, lineage, quality, privacy, retention, RBAC. - Delivery: Azure DevOps, Git, CI/CD, Agile and DevOps practices.