AWS Senior Data Engineer

Qualifications Bachelor's Degree in Information Technology, Computer Science, Information Systems, Data Management, Engineering, Mathematics or related discipline. AWS Cloud Practitioner Certification as a minimum. Relevant Business Analysis, Agile and Data certifications will be advantageous. Experience Mandatory Minimum 8 years professional experience. Minimum 5 years experience within Financial Services. Minimum 5 years Business Analysis or Feature Analysis experience. Minimum 5 years Data Analysis experience. Experience working within Agile delivery environments. Experience engaging senior business and technology stakeholders. Functional Skills Requirements Elicitation and Management. Business Analysis. Feature Analysis. User Story Development. Acceptance Criteria Definition. Workshop Facilitation. Process Analysis. Data Analysis. Data Mapping. Data Modelling. Data Governance. Data Lineage. Data Quality Analysis. Stakeholder Management. Backlog Management. Agile Delivery Support. Testing Support and Validation. Technical Skills SQL. Data Profiling. Data Warehousing Concepts. Data Architecture Concepts. Metadata Management. Data Lineage Tooling. Reporting and Analytics Concepts. API and System Integration Understanding. Jira and Agile Tooling. Highly Advantageous Experience Foreign Exchange (FX) domain knowledge. Capital Markets experience. Treasury systems exposure. Front Office Trading environments. Regulatory Reporting. AWS Cloud Data Platforms. Data Migration Programmes. Enterprise Data Management frameworks. Financial Product Data Models. Key Success Criteria The resources shall be deemed successful when: Business and data requirements are documented and approved. User stories and acceptance criteria are production-ready. Target-state data models are documented and validated. Data lineage and ownership artefacts are completed. Critical data elements are identified and catalogued. Cross-stream dependencies are analysed and managed. Data quality issues are identified with remediation recommendations. Architecture and implementation decisions are supported through evidence-based analysis. Programme delivery milestones are supported without delays attributable to analysis activities.