AWS Senior Data Engineer
Summary
Senior data role in financial services titled AWS Senior Data Engineer: day to day it is analysis-heavy work — eliciting and documenting business/data requirements, writing user stories and acceptance criteria, data modelling, lineage, governance and quality analysis on AWS cloud data platforms within Agile delivery. Requires 8+ years' experience, 5+ in financial services, SQL, and AWS Cloud Pract
Qualifications
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
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
- 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.
