Partner with business and technology teams to rapidly prototype and deploy analytics and tooling, leveraging AI/ML and innovative approaches
Implement and manage platform controls, including access, security, and compliance, ensuring all data and AI solutions meet firmwide SDLC standards
Provide transparency and drive executive visibility into bottlenecks, progress, performance metrics, and adoption tracking in making AI-ready and critical data sources available for innovation
Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements; embed evergreen controls on data flows to improve safety, transparency, and traceability
Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows
Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques, then fix the cause and embed uplifted evergreen controls to prevent future failures
Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience and driving operational efficiency
Demonstrate control environment improvements and reduction in toil through common tooling and frameworks; uplift the metadata (semantic layer) of existing data to support AI and Natural Language Query (NLQ) usage, accelerate adoption of Mesh data architecture, reduce consumer friction, and deliver data product prototypes
Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements
Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations
Required Qualifications, Capabilities, and Skills
Formal training or certification on software engineering concepts and applied experience
Experience and awareness in working within Risk Analytics space; preferred knowledge of corporate bond investment assets and structured credit products such as securitisation (e.g., CLO, CMBS, RMBS, ABS)
Proven experience integrating AI-assisted development tools (e.g., Claude Code, Copilot, or similar) into engineering workflows
Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs
Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, DataBricks, cloud platforms)
Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloguing
Excellent communication skills with the ability to convey complex technical concepts to diverse audiences, including executive leadership
Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls
Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity
Ability to review and validate AI-assisted outputs (e.g., code, model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements
Hands-on practical experience delivering system design, application development, testing, and operational stability
Preferred Qualifications, Capabilities, and Skills
Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms
Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale
Familiarity with business-led analytics delivery models and rapid prototyping frameworks
Experience with AI/ML governance, prompt engineering, and integrating AI tools into SDLC
Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment)
Understanding of agile and product management methodologies and experience working in agile teams
Ability to multi-task in a fast-paced environment and operate independently with minimal supervision; strong judgment with the ability to balance strategic vision with pragmatic, incremental delivery
Experience building and growing capabilities and developing talent in data science or data management teams
Excellent interpersonal skills and ability to build strong working relationships with business, technology, and control stakeholders across global teams
