Product Owner - WM Data Platforms for Data Warehouse
Morgan Stanley is hiring a Product Owner - WM Data Platforms for Data Warehouse in Budapest, Hungary. Level rates it ; you can apply on Morgan Stanley.
Level's read on this job
- On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
- Level classes it as a mid-level role.
- Morgan Stanley has 1,200+ other open roles on Level.
AI in this role
The Product Owner for the Data Warehouse is responsible for defining and delivering data platform capabilities that support business reporting, analytics, regulatory, and operational needs. This role partners closely with business stakeholders, data engineering, architecture, governance, and technology teams to translate requirements into a prioritized product backlog and ensure delivery of scalable, reliable, and well-governed data solutions.
Key responsibilities include managing the product roadmap, gathering and refining requirements, prioritizing features and enhancements, defining acceptance criteria, supporting agile delivery ceremonies, and ensuring data warehouse solutions align with Wealth Management data standards, controls, and business objectives. The Product Owner also helps drive adoption, resolve delivery blockers, and continuously improve data quality, usability, performance, and stakeholder satisfaction.
Responsibilities
- Own and manage the product roadmap for data warehouse capabilities, enhancements, and platform modernization initiatives.
- Partner with business stakeholders to understand reporting, analytics, operational, and regulatory data needs.
- Translate business requirements into clear user stories, acceptance criteria, and prioritized backlog items.
- Work closely with data engineering, architecture, QA, and operations teams to support agile delivery.
- Prioritize features, defects, technical debt, and data quality improvements based on business value and risk.
- Ensure delivered solutions align with Wealth Management data governance, data quality, lineage, and control standards.
- Drive improvements in data quality, availability, performance, usability, and stakeholder adoption.
- Facilitate sprint planning, backlog refinement, demos, and stakeholder review sessions.
- Identify delivery risks, dependencies, and blockers, and help coordinate resolution across teams.
- Define success metrics and monitor product outcomes, usage, and stakeholder satisfaction.
- Act as the primary point of contact for business users and technology teams on data warehouse product priorities. Experience
Requirements
- Bachelor's or Master's degree in Data Science, Engineering, Business, Finance, or a related field.
- 4+ years of experience in wealth management, data strategy, or analytics.
- Strong understanding of Wealth Management, Middle Office, Data Management, and Data Governance principles.
- Experience serving as a Product Owner, Business Analyst, or Data Steward for enterprise data platforms.
- Experience managing and prioritizing product backlogs, epics, features, and user stories within Agile delivery frameworks.
- Experience with Jira, Confluence, and Agile methodologies to document business requirements, manage sprint planning, track delivery progress, and support release management activities.
- Strong analytical and problem-solving skills.
- Excellent written and verbal communication skills with the ability to present complex concepts to both business and technical audiences.
#LI-hybrid #LI-NB1 BPDBA
WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Certified Persons Regulatory Requirements:
If this role is deemed a Certified role and may require the role holder to hold mandatory regulatory qualifications or the minimum qualifications to meet internal company benchmarks.
Flexible work statement
Interested in flexible working opportunities? Morgan Stanley empowers employees to have greater freedom of choice through flexible working arrangements. Speak to our recruitment team to find out more.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.
How we rate this
Product Owner - WM Data Platforms for Data Warehouse at Morgan Stanley rates 23 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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