Vallum Associates
Data Product Owner

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Principal accountabilities and responsibilities:
- Lead product delivery of data foundations for Operational Resilience, providing a governed, reusable data layer that underpins how the bank measures resilience, maps service dependencies and tests scenario assumptions across Procurement and Real Estate Services.
- Own the roadmap and backlog for one or more data products (e.g. procurement spend, supplier performance, real estate portfolio performance, operational KPIs), aligned to business outcomes and strategic data model.
- Translate stakeholder needs into clear product requirements i.e. problem statement, user journey, use cases, acceptance criteria, value hypotheses, and measurable outcomes (OKRs, KPIs).
- Drive end-to-end delivery through the full data product lifecycle, ensuring reusability and consistent consumer experience.
- Partner with Data Engineers, Analysts, Architects, and Data Governance to define data models, data contracts, metadata, lineage, quality rules, retention and access controls in line with HSBC standards and policies.
- Publish Data Products to the Data Fabric with appropriate documentation, certification, and consumption guidance (e.g. semantic definitions, metric logic, SLAs).
- Prioritise delivery using Agile practices (refinement, sprint planning, demos, retrospectives), managing dependencies and removing blockers across multiple teams.
- Oversee data quality assessment and continuous improvement (completeness, accuracy, timeliness, consistency), including monitoring and incident resolution where required.
- Drive adoption & stakeholder engagement, communications, training materials, and enablement for consumers (MI / reporting teams, analysts, operational users).
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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- Strong understanding of Operational Resilience concepts and hands-on experience with relevant data e.g. service / impact tolerances, asset dependency incidents / outages, DR / BCP test evidence, concentration indicators.
- Conceptual understanding of Procurement / Real Estate and associated data domains preferred.
- Demonstrable experience in a data-related role (data product, analytics, data operations, data engineering, MI delivery) with ownership of outcomes.
- Strong capability in requirement gathering & analysis, stakeholder engagement, and use case management, able to move from ambiguity to a prioritised plan.
- Working knowledge of data architecture, data management, data governance concepts.
- Experience building or managing data assets aligned to business outcomes and strategic data models (e.g. curated datasets, KPIs, semantic models, reports).
- Understanding of data architecture and data modelling (conceptual / logical), and how design decisions impact downstream consumption and scalability.
- Practical experience with data quality assessment, uncovering inconsistencies across systems, and driving remediation with accountable owners.
- Familiarity with modern data platform patterns (e.g. metadata-driven discovery) and operating models for reusable data products.
- Strong product delivery skills (backlog management, prioritisation, MVP definition, release planning), experience with JIRA and Confluence.
- Ability to communicate through clear narratives and practical value cases, comfortable presenting solutions to technical and non-technical stakeholders.
- Technical literacy, confidence working with SQL, python, analytics tooling, experience with cloud data platforms (GCP, Big Query).
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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