Key Responsibilities Lead the technical delivery of complex data engineering projects, ensuring solutions are scalable, secure, and aligned with our delivery framework, and client goals. Design and build high-quality data pipelines and integration workflows, setting the technical direction and ensuring engineering best practices are followed throughout the development lifecycle. Collaborate with multidisciplinary teams, including a wide range of other roles, to shape solutions that meet both technical and business requirements. Mentor and support data engineering teams, fostering a culture of continuous improvement, knowledge sharing, and technical excellence. Support testing activities by ensuring pipelines are testable, observable, and reliable; work with QA and analysts to define test strategies, implement automated tests, and validate data quality and integrity. Contribute to technical planning, including estimation, risk assessment, and defining delivery approaches for client engagements and new opportunities. Engage with clients and stakeholders, translating data requirements into technical solutions and communicating complex ideas clearly and effectively. Champion engineering standards, contributing to the development and adoption of data engineering guidelines, design patterns, and delivery methodologies that contribute to our delivery framework. Stay current with emerging technologies, evaluating their relevance and potential impact, and promoting innovation within the firm and clients. Contribute to internal capability building, helping shape data engineering practices, tools, and frameworks that enhance delivery quality and efficiency. Essential competencies Strong communicator, able to clearly articulate technical concepts to both technical and non-technical stakeholders. Confident working independently or as part of a collaborative, cross-functional team. Skilled at building trust with clients and colleagues, with a consultative and solution-focused approach. Demonstrated leadership and mentoring capabilities, supporting the growth and development of engineering teams. Organised and adaptable, with excellent time management and the ability to respond to shifting priorities. Self-motivated, proactive, and committed to continuous learning and improvement. Creative problem-solver with the ability to think critically and deliver innovative, practical solutions. Team-oriented, with a positive attitude and a strong sense of ownership and accountability. Technologies, Methodologies and Frameworks: Direct delivery experience using cloud-native data services, specifically in Microsoft Azure, Fabric, Dataverse, Synapse, Data Lake, Purview. Deep expertise in data engineering tools and practices, including Python, SQL, and modern ETL/ELT frameworks (e.g., Azure Data Factory, Talend, dbt). Experience designing and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding of data security and compliance best practices, including secure data handling and regulatory requirements (e.g., Secure by design). Comfortable working in agile, multi-disciplinary teams, contributing across the full delivery lifecycle and supporting continuous improvement. Adaptable and quick to learn new tools, frameworks, and technologies to meet the needs of diverse client projects.