Senior Data Engineer

Design, build and deliver robust data solutions across 7IM, and provide senior technical leadership within the data engineering team. This role combines hands-on engineering with technical ownership of complex data pipelines and platform components, making sure solutions align with our architectural standards and business goals. You will mentor other engineers, contribute to design and architecture decisions, and champion engineering best practices., * Mentor and support other data engineers, sharing knowledge and raising the bar on quality.

• Contribute to the data architecture and roadmap, keeping delivery aligned with business strategy and objectives.
• Design, develop, test and release robust, scalable software solutions using modern technologies and best practices.
• Develop and maintain data models, pipelines, processes and documentation across the platform.
• Ensure technical excellence, adherence to standards and minimisation of technical debt.
• Help define and uphold engineering standards, contributing to meaningful metrics and KPIs.
• Collaborate with business stakeholders and technical teams to analyse needs, propose solutions and ensure alignment.
• Drive initiatives to modernise technology, tools and development processes.
• Peer review code, documentation, and architectural artefacts for accuracy, maintainability and supportability.
• Provide third-line support and guidance to application support teams.
• Foster innovation, identify opportunities for technical efficiencies and communicate best practices.
• Ensure compliance with regulatory standards and internal policies.
• Other duties as reasonably required by line management and 7IM.

• Relevant degree or equivalent experience.
• Azure DP-203 (or equivalent)
• Certification in Data Governance and Stewardship Professional (DGSP) or similar (desirable).
• Evidence of business experience or formal business qualifications (desirable)., * Proven experience in senior data engineering roles (e.g., Senior Data Engineer, Data Engineer) within regulated environments.
• Knowledge of the following is required:
• Modern Data platform concepts: Data Lake, Lakehouse, Data Warehouse, Data Vault
• Azure Data Technologies; Azure Databricks, Azure Data Lake Storage, Azure Data Factory, , MS Fabric
• ETL / ELT processes and designing, building and testing data pipelines
• Building data transformations using python /PySpark
• Azure Cloud Version control and CI/CD tools, specifically Azure DevOps Service
• Analytics and MI products including MS Power BI
• Data catalogue & governance using MS Purview
• Knowledge of the following would be desirable:
• Microsoft server-based data products (SQL Server, Analysis Services, Integration Services and Reporting Services)
• Enterprise Architecture tools (e.g. LeanIX, Ardoq), Frameworks (TOGAF) and core artefacts (Capability Models, Technical Reference Models, Data Flow Diagrams
• Analytics and MI products including MS Power BI
• Experience developing and implementing technology roadmaps and target state architectures.
• Experience integrating bespoke software, commercial off-the-shelf packages and third-party services.
• Demonstrable experience of migrating on-premises workloads to cloud-native services
• Excellent analytical, problem-solving and communication skills.
• Strong stakeholder management and influencing abilities at all levels.
• Ability to work efficiently under pressure and lead teams in adverse situations.
• Commitment to continuous learning and keeping skills current.

Skills

• Able to work autonomously within the data space, including when navigating ambiguity.
• Must be comfortable making decisions and able to effectively execute the plan in a fast-paced environment.
• Excellent verbal and written communication with a proven track record of stakeholder engagement and influencing both business and technical stakeholders
• Ability to communicate between the technical and non-technical - interpreting the needs of technical and business stakeholders, communicating how activities meet strategic goals and client needs.
• Ability to analyse data to drive efficiency and optimisation, design processes and tools to monitor production systems and data accuracy.
• Ability to produce, compare, and align different data models across multiple subject areas, reverse-engineering data models from a live system where required.
• Excellent analytical and numerical skills are essential, enabling easy interpretation and analysis of large volumes of data

• Excellent problem-solving and data modelling skills (logical, physical, semantic and integration models) Other relevant information

• Experience in wealth management (including operational knowledge) would be advantageous
• Prior experience working in Financial Services preferred; thorough understanding of data security, data privacy, GDPR required
• Certification in TOGAF or equivalent structured architecture framework (desirable). Not Specified