At a Glance
- Tasks: Own and manage data products, ensuring they meet business needs across various teams.
- Company: Join a leading firm in the energy and financial services sector.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and collaboration across divisions.
- Why this job: Be the bridge between tech and business, driving impactful data solutions.
- Qualifications: 3-5 years in product ownership or data analysis, with strong stakeholder management skills.
The predicted salary is between 60000 - 80000 £ per year.
Key Responsibilities:
- Data Product Ownership
- Maintain a consolidated, group-wide view of data requirements across Analytics/Trading, Asset Management, the SEO platform, and any other relevant areas.
- Own the data product backlog: intake, refinement, prioritisation, and progress tracking.
- Develop and maintain a clear understanding of the capabilities of each data product being built, and how they can address problems across the group.
- Define and maintain data product standards: schemas, naming conventions, quality criteria, and documentation.
- Stakeholder Management
- Act as the primary point of contact for non-technical stakeholders across all divisions requesting data support.
- Translate business requirements from Commercial, Trading, Asset Management, and other teams into concrete, well-scoped data engineering tasks.
- Communicate progress, priorities, and constraints clearly across technical and non-technical audiences.
- Data Quality & Governance
- Monitor data quality and coverage across production datasets.
- Identify gaps, elevate issues, and coordinate remediation with the engineering team.
- Drive consistent data definitions and documentation standards across the group.
- Architecture & Delivery
- Work with the Senior Data Engineer on architecture decisions and pipeline design, bringing the user requirements perspective.
- Ensure data products are fit for purpose and meet the needs of downstream consumers across all divisions.
Experience & Qualifications:
- Essential Skills
- Demonstrated product ownership experience, ideally in a data or analytics context.
- Ability to maintain and communicate a structured backlog across multiple concurrent workstreams.
- Strong stakeholder management: able to gather, challenge, and refine requirements from non-technical teams across different parts of a business.
- Clear written and verbal communication.
- Sufficient technical literacy to engage credibly with data engineers: comfortable reading SQL and understanding pipeline concepts.
- Desirable
- Background in data science or data analysis.
- Experience with data cataloguing, data governance, or data quality tooling.
- Familiarity with energy, commodity markets, or financial services data.
- Experience working across multiple divisions or business units in a cross-functional data role.
- Awareness of dashboarding tools (Grafana, Power BI, or similar).
Profile
- 3–5 years' experience in a product ownership, data analysis, or data product role.
- Product instincts and a structured approach to translating business need into delivery.
- Comfortable operating as the interface between non-technical stakeholders and a specialist engineering team.
- Able to develop a holistic view of data capabilities across the group and apply them to problems from multiple divisions.
- High ownership mindset and attention to output quality.
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We think this is how you could land Data Product owner
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We think you need these skills to ace Data Product owner
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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How to prepare for a job interview at Mint Selection
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
