Data Architecture
• Owns and defines end-to-end data architecture for complex, enterprise-scale data initiatives.
• Designs and reviews high-level and detailed solution architectures, including logical and physical data models.
• Ensures architectural solutions comply with enterprise standards, governance, security, and regulatory requirements.
• Works closely with IT, business stakeholders, and enterprise architecture teams to identify requirements, pain points, and optimal solutions.
• Develops and maintains architectural standards, patterns, guardrails, and best practices for data integration and platforms.
• Builds stakeholder confidence by ensuring data quality, lineage, and trustworthiness for decision-making.
⸻
Data Engineering
• Leads the design, build, operationalisation, security, and monitoring of data pipelines and data stores.
• Ensures solutions balance functional and non-functional requirements such as performance, resilience, cost, and scalability.
• Oversees adherence to data standards, architectures, and security controls.
• Contributes to organisational policies, standards, and guidelines for data engineering practices.
⸻
Data Visualisation
• Leads the selection and adoption of data visualisation approaches and tools (e.g., Power BI, Streamlit, React).
• Defines the purpose, scope, and parameters for data visualisations aligned to business needs.
• Ensures effective communication of insights using appropriate textual, numerical, and graphical techniques.
• Advises stakeholders on the appropriate use of data visualisation to support decision-making.
⸻
Data Science and Advanced Analytics
• Supports all stages of data science and analytics solution development.
• Identifies and evaluates appropriate data sources to support predictive and analytical use cases.
• Advocates standards and best practices for developing, evaluating, deploying, and monitoring analytics, ML, and AI solutions.
⸻
Financial Management
• Advises on financial planning, budgeting, and cost optimisation for data platforms and solutions.
• Develops financial forecasts and monitors expenditure against agreed tolerances.
• Analyses variances and provides recommendations to optimise use of available budgets.
⸻
Stakeholder and Relationship Management
• Identifies stakeholder communication and engagement needs.
• Acts as a trusted advisor and single point of contact for data architecture topics.
• Facilitates open communication and informed decision-making across technical and business stakeholders.
• Translates complex technical concepts into clear business outcomes.
⸻
Requirements Definition and Management
• Leads scoping, requirements definition, prioritisation, and change management for large and complex initiatives.
• Applies appropriate predictive or agile delivery methods as required.
• Negotiates priorities and resolves conflicts across diverse stakeholder groups.
• Establishes and maintains requirements baselines and governance processes.
⸻
Methods and Tools
• Promotes adoption of approved methods, tools, and standards across programmes and teams.
• Evaluates, selects, and implements appropriate tools aligned to organisational policies.
• Reviews benefits and effectiveness of tools and recommends continuous improvements.
⸻
Line Leadership Contributions
Performance and Delivery
• Supports the Data Product Manager in driving high-performance delivery across data product teams.
• Leads Agile/SAFe ceremonies to ensure prioritised and timely delivery.
• Supports definition of standards, tooling, and ways of working.
Employee Experience and Development
• Encourages inclusive, healthy, and motivating working practices.
• Provides guidance on career development and long-term professional growth.
• Mentors and coaches team members, fostering a strong data culture.
Your Profile
Essential skills/knowledge/experience:
Extensive Knowledge
• Cloud data platforms: Microsoft Fabric, Snowflake, Azure, Databricks
• Data integration, ETL/ELT, CDC, streaming, APIs, and messaging
• Data modelling (relational, dimensional, NoSQL)
• Data governance, security, privacy, and regulatory compliance
• Power BI, Azure ML, ML/AI, Generative and Agentic AI
Strong Experience
• End-to-end implementation of cloud data platforms
• Advanced Snowflake capabilities and cost optimisation
• Complex data migrations from enterprise source systems
• Data security, RBAC, access controls, and compliance frameworks (SoX, GDPR)
• Troubleshooting complex data pipeline and platform issues
Qualifications
Essential
• Significant professional experience in data architecture, data engineering, or data management disciplines
Desirable
• Bachelor’s or Master’s degree in Computer Science, Data Management, Statistics, Applied Mathematics, or related field