Experience
- Experience in a data engineering role, ideally within a regulated environment
- Knowledge of the following is required:
- Modern Data platform concepts; Data Lake, Lakehouse, Data Warehouse, Data Vault
- Azure Data Technologies; Azure Data Lake Storage, Azure Data Factory, Azure Databricks, 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
- 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
- Commitment to continuous learning and keeping skills current
Skills
• Able to work independently within the data space and comfortable dealing with some ambiguity
• Comfortable delivering to 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)
Qualifications
• 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).
Other relevant information
• Experience of wealth management (including operational knowledge) would be advantageous
• Prior experience working in Financial Services preferred thorough understanding of data security, data privacy, and GDPR required