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Embark on a transformative journey as a Fraud Technology Data Product Owner - AVP at Barclays. Our vision is clear - to redefine the future of banking by crafting innovative solutions. This is a high-impact opportunity at the intersection of risk, innovation, and strategic influence, essential to safeguarding the bank’s integrity and customer trust. You will assess requirements and translate them into technical details to execute according to specifications while managing delivery and expectations with stakeholders across the wider Fraud team.
Job Responsibility:
Identification and analysis of business problems and client requirements that require change within the organisation
Development of business requirements that will address business problems and opportunities
Collaboration with stakeholders to ensure that proposed solutions meet their needs and expectations
Support the creation of business cases that justify investment in proposed solutions
Conduct feasibility studies to determine the viability of proposed solutions
Support the creation of reports on project progress to ensure proposed solutions are delivered on time and within budget
Creation of operational design and process design to ensure that proposed solutions are delivered within the agreed scope
Support to change management activities, including development of a traceability matrix to ensure proposed solutions are successfully implemented and embedded in the organisation
Requirements:
Defining, prioritizing, and delivering data platform capabilities that enable large‑scale cloud migrations, especially within regulated environments such as fraud, risk, compliance, or enterprise data management
On-Prem / AWS data architectures and services, including Oracle, Hadoop, Abinitio, S3, DBT, Redshift, Databricks and data streaming frameworks, with considerable understanding of data ingestion, transformation, lineage, metadata management, and cloud data modeling
Leading end-to-end data migration projects, including legacy-to-cloud mapping, target-state schema definition, dependency identification, and migration readiness assessments
Communicating product strategy, backlog priorities, and delivery milestones effectively to senior stakeholders, engineering teams, architecture groups, and cross-functional partners involved in cloud transformation
Applying a data-driven and analytical mindset to evaluate migration progress, measure platform performance, identify blockers, and drive continuous improvement across ingestion pipelines, quality controls, and consumption pathways
Nice to have:
Ensuring adherence to legal, regulatory, security, and governance standards throughout the migration lifecycle, including data classification, control enforcement, encryption, and responsible data usage on AWS
Developing a clear and actionable product roadmap for cloud migration phases—covering ingestion, curation, validation, entitlement, and end-state enablement for enterprise platforms such as fraud analytics, modeling, and investigations
Collaborating with cross-functional teams including cloud engineering, data engineering, architecture, data governance, security, compliance, fraud SMEs, and program management to deliver a secure, scalable, and resilient cloud data environment
Driving stakeholder alignment and dependency management, ensuring upstream and downstream systems, controls teams, and analytics partners are ready for the transition and can consume the migrated datasets seamlessly