A Data Product Manager owns the vision, strategy, and delivery of “data as a product” – things like semantic layers, dashboards, metrics platforms, datasets, APIs, and decision tools that other teams rely on. They sit at the intersection of business, data, and engineering: understanding how different functions use data, defining clear data products (with documented contracts, SLAs, and definitions), and working with Data Engineers, Analysts, and Architects to make those products reliable, discoverable, and easy to use. Their job is to ensure data products solve real user problems, are trusted, and continuously improve based on usage, feedback, and changing business needs.
Why In Demand
Explosion of self-service analytics & AI – As more teams want to explore data and build models themselves, organisations need someone to design and manage robust, reusable data products instead of one-off reports.
Need for “one version of the truth” – Conflicting metrics and definitions are costly; Data PMs own the standardisation of KPIs and data contracts, so decisions are based on consistent, trusted numbers.
Bridging business and data teams – They translate business problems into data requirements and product roadmaps, ensuring data platforms are built around real use cases and value, not just technology.
Scaling data usage across the organisation – Data PMs focus on adoption, UX, documentation, and training for data products, turning a central data platform into something everyone can actually use.
Measurable ROI from data investments – They treat data like a product portfolio, tracking usage, impact, and satisfaction, and prioritising work. Hence, data spend turns into clear improvements in revenue, cost, and risk.
Problems Solved
Data Product Managers solve the problem of organisations having lots of data but very little usable, trusted data that people can rely on to make decisions. Without them, teams end up with duplicated dashboards, conflicting KPIs, hard-to-find datasets, and fragile one-off solutions built for a single project. A Data Product Manager treats datasets, metrics, and data tools as products with clear owners, users, roadmaps, and success criteria—making sure they are valuable, reliable, well-documented, and continuously improved.
- Turn raw data into usable products – They define clear data products (datasets, metrics layers, APIs, dashboards) with documented scope, owners, SLAs, and user journeys. Hence, people know what to use and when.
- Create one version of the truth – They standardise key metrics, definitions, and data contracts across teams, reducing time wasted arguing over numbers and enabling faster, aligned decision-making.
- Prioritise high-impact data work – They gather requirements, quantify impact, and build a roadmap so Data Engineers, Analysts, and platform teams focus on the data products that deliver the most value.
- Drive adoption and trust – They invest in UX, documentation, training, and feedback loops so data products are easy to discover, understand, and use—raising data literacy and confidence across the business.
- Measure and improve data ROI – They track usage, satisfaction, and business outcomes for each data product, using these insights to refine features, retire low-value assets, and justify future investment.
- Align data with strategy and operations – They connect data products to real workflows (e.g., sales, supply chain, finance) and strategic goals, ensuring data is embedded in everyday decisions rather than an afterthought.
Skills Needed
| Skill Category | Skills (with importance /10) |
|---|---|
| Technical | Data platform basics – warehouses/lakes (9), API & integration basics (7), SQL literacy (7), BI / dashboard tool familiarity (6), Hands-on coding in production systems (2) |
| Digital & Data | Data modelling concepts (facts/dimensions, entities) (9), Data lineage & metadata understanding (8), Event tracking design for products (8), Using query tools & dashboards day-to-day (8), Authoring complex ETL/ELT pipelines yourself (3) |
| Problem-Solving | Framing business problems as data products (10), Using prioritisation frameworks (RICE/ICE) (9), Decomposing complex data domains into scopes (8), Root-cause analysis on data quality/consistency issues (7), Formal optimisation / OR methods (2) |
| Analytics | Defining product & data KPIs/health metrics (9), Cohort, usage & adoption analysis (8), Basic experiment/A-B test literacy (7), Quick SQL/Spreadsheet deep-dive analyses (7), Advanced ML/statistical modelling yourself (3) |
| Communication | Working with data engineers & analytics engineers (10), Partnering with domain/business teams on requirements (9), Facilitating workshops for requirements/prioritisation (8), Bridging central vs domain data teams (8), Running significant cross-org change events (4) |
| Collaboration | Working with data engineers & analytics engineers (10), Partnering with domain/business teams on requirements (9), Facilitating workshops for requirements/prioritisation (8), Bridging central vs domain data teams (8), Running large cross-org change events (4) |
| Leadership | Owning outcomes for key data products (10), Influencing without formal authority across teams (9), Driving “data as a product” mindset & practices (8), Coaching stakeholders on using data products effectively (7), Formal people management of a big team (4) |
| Business | Understanding business model & key KPIs (9), Estimating value/ROI of data product work (8), Knowing core business processes that data supports (7), Reading basic financials & business cases (6), Detailed corporate finance/tax structuring (1) |
| Strategic | Defining vision for core data products & domains (9), Building & maintaining a data product roadmap (9), Balancing foundational work vs quick wins (8), Aligning data products with data & corporate strategy (8), Enterprise-wide portfolio strategy design (3) |
| Customers | Empathy for internal data consumers (analysts, ops, execs) (10), Running discovery interviews & feedback sessions (8), Mapping data user journeys & pain points (8), Managing different personas & data maturity levels (7), Running large external user councils/events (3) |
| Stakeholders | Managing expectations on what data can/can’t answer today (10), Negotiating priorities across functions/regions (9), Running steering/prioritisation sessions (8), Resolving conflicts over metrics/definitions (8), Presenting to board/C-suite when required (4) |
| Adaptability | Learning new data tools/platforms quickly (9), Adapting to changing requirements & constraints (9), Comfort with ambiguity & incomplete data (9), Switching business domains when needed (7) |
| Governance | Writing precise specs, docs & release notes (10), Explaining data concepts in simple language (9), Storytelling around data product roadmap & impact (8), Crisp async comms (Slack/email/docs) (8), Public conference talks / thought leadership (3) |
