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.