Data Architect
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Sword is a leading provider of business technology solutions within the Energy, Public and Finance Sectors, driving transformational change within our clients. We use proven technology, specialist teams and domain expertise to build solid technical foundations across platforms, data, and business applications. We have a passion for using technology to solve business problems, working in partnership with our clients to help in achieving their goals.
The Data Architect is responsible for defining and implementing scalable, secure, and efficient data architectures that enable our client to realise business value from its data assets. This role ensures that data solutions are aligned to business needs, support strategic objectives, and adhere to best practice standards across the data lifecycle.
- Lead workshops, interviews, and discovery sessions to understand business processes, data needs, and strategic objectives.
- Collaborate effectively with both technical and non-technical stakeholders to ensure alignment between business requirements and data solutions.
- Define and own the target state data architecture, including data domains, patterns, layers, and consumption models.
- Design scalable and reusable data structures and define data dependencies across systems and produce detailed technical design and data architecture artifacts
- Produce high-quality functional designs, including both high-level architecture and detailed low-level designs.
- Establish and maintain principles and standards for data ingestion, transformation, storage, metadata management, and access.
- Work closely with data governance teams to define and enforce data quality rules and metadata management practices.
- Ensure compliance with organisational standards, regulatory requirements, and industry best practices.
- Specify and ensure adherence to non-functional requirements such as scalability, performance, availability, resilience, monitoring, disaster recovery, and maintainability.
- Design data pipelines and transformation processes that are reusable, testable, and resilient.
- Support the implementation of efficient data processing solutions across batch and real-time architectures where appropriate.
- Ensure documentation is clear, structured, and accessible to relevant stakeholders.