Responsibilities
- Architect and manage data workflows across Microsoft SQL Server and Azure Synapse Analytics platforms
- Build and sustain Spark SQL notebooks for advanced data processing and analytical tasks
- Convert business and financial logic into clean, sustainable code implementations
- Develop data validation frameworks and integrity checks in coordination with quality assurance teams
- Establish automated data quality monitoring and reconciliation procedures
- Support the creation and upkeep of anonymized, comprehensive test datasets covering edge cases
- Examine live data to detect irregularities, troubleshoot stored procedures and notebooks, and fix data issues
- Persistently investigate underlying causes of data problems, diving deeply until resolved
- Advise on database design choices that balance efficiency, scalability, and long-term maintainability
- Enhance query speed and data processing efficiency for large financial datasets
- Design and deploy solutions using Azure Data Factory, Delta Lake, and associated tools
- Maintain a holistic view of data movement while focusing on precise implementation details
- Produce and update detailed documentation for database structures, processes, and data pathways
- Generate visual process diagrams using tools like Lucidchart, Visio, dbt, or Azure Purview
- Record data transformation rules and calculation methods for audit and regulatory compliance
- Help define and promote data governance policies and engineering best practices
- Serve as primary escalation contact for critical production data incidents
- Work with test automation specialists to build data-centric testing approaches and validation scripts
- Collaborate with engineering teams using Azure DevOps to support CI/CD pipeline development
- Support both centralized data initiatives and product-focused engineering goals through stakeholder coordination
Benefits
- Access to large-scale financial data systems involving intricate calculations
- Chance to influence data engineering standards across the company
- Experience with current Azure cloud platforms and cutting-edge technologies
- Interaction with experienced professionals in testing, software development, and data engineering
- Matrix organizational model offering varied learning from different departments
- Flexible hybrid work setup
- Engineering environment that emphasizes quality, precision, and technical excellence
- Support for professional development, tools, and training
- Impactful contributions to systems affecting financial accuracy and business outcomes
Work Arrangement
Hybrid — UK
Team
Matrix organization with cross-functional collaboration between Data CoE and Engineering teams
Reports to
Data Centre of Excellence Team Lead
