Data Product Managers in Chinese Tech Companies
How Chinese tech companies build data products—and whether Data PMs are the missing puzzle piece
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Product managers build features—from intuitive user interfaces to robust API integrations or scalable backend infrastructure. But what about data products? Do product managers typically build or deeply understand data-driven solutions?
Ray, a veteran of China's top tech firms (Alibaba, Tencent, Baidu, and Xiaohongshu/Rednote), gave us a peek behind the curtain during a recent podcast episode, offering valuable insights into a role that's rarely seen in the U.S.—the Data Product Manager. More than just a niche title, it reflects a very different philosophy in how Chinese companies structure responsibilities around data, product, and business impact.
Wait, What Is a Data Product Again?
Data products are not just glorified dashboards. Ray dropped some intriguing examples of powerful data products already shaping China's business landscape.
Baidu Index, for instance—a product similar to Google Trends:
🎙️ Search for any keyword, like 'data analysis,' and Baidu Index reveals its search trends over the past decade. Marketers and businesses leverage this to gauge consumer interest and shape their strategy.
Another good example is Taobao’s Business Advisor, essentially a personal data-driven coach for small businesses:
🎙️ It tells shop owners exactly what products are trending. If you sell women's clothing, it identifies not just broad trends, but specific sub-categories—right down to style and color—providing direct recommendations on what to sell next.
And What Exactly Is a Data Product Manager?
🎙️ Data products cover the entire data workflow—from collection, storage, processing, all the way to analysis. Instead of handling tasks manually or on a case-by-case basis, Data Product Managers automate these processes into scalable products.
The role of data product manager didn’t just randomly sprout overnight; it evolved organically. The role is also a product of Chinese tech corporate culture. Large corporations in China are known for dividing work into highly specialized, compartmentalized roles. Developers, including data scientists and algorithm engineers, are expected to focus solely on the technical execution. The responsibility of understanding the full business context—and translating it into actionable product direction—falls squarely on the shoulders of the product manager.
From Data Analyst To Data Product Manager
Ray spent over a decade navigating the data trenches at China's internet titans. He reflects on this journey:
🎙️ I started out in data analytics, then transitioned to strategy products—a role that might not even exist in the U.S. Over the last seven years, my role focused intensely on data product management.
So what's the appeal?
The job of a Data Product Manager goes far beyond simply interpreting numbers. It involves designing end-to-end systems that convert raw data into usable, actionable tools. Taking BI dashboards as an example: it’s not just about building a visualization interface, but deciding which metrics matter, how to structure the underlying data, how users will interact with the reports, and what kind of decisions the dashboard is meant to drive.
Data PMs work across the entire data lifecycle—defining how data should be captured, stored, processed, and accessed—with a focus on usability and scalability. They collaborate with engineering teams to productize workflows, from behavior tracking and event data pipelines to automated dashboards and recommendation logic. Just as importantly, they translate complex data capabilities into intuitive, value-driving features for the business.
So... Who Should Own the Data Product?
However, Ray didn’t shy away from the real talk. Data Product Managers aren’t magicians—there are very real limitations and misconceptions.
There's also the tricky cultural balancing act. In many companies, data professionals grapple with demonstrating their strategic value clearly.
Ray pointed out a key tension: data teams sometimes take on aspects of product management—such as identifying specific user needs or designing metrics—and when they do, they’re often praised for their initiative and rewarded for "going beyond" their technical remit. Meanwhile, Data Product Managers, who take on this type of work systematically and by design, often see their contributions underrecognized. Their work—spanning cross-functional alignment, scalable data platform design, and long-term workflow optimization—is less visible and less understood, even though it directly enables strategic impact across the organization.
🎙️ In China, product managers traditionally own features, data teams own data—but Data Product Managers bridge these two worlds, and sometimes that leads to internal confusion about roles and responsibilities.
How Does This Compare to the U.S.?
Interestingly, the approach in the U.S. tends to differ significantly. It’s relatively rare to find product managers dedicated solely to data teams—and even when such roles exist, they’re often filled by individuals with general product management experience rather than deep, hands-on data expertise. Consequently, data teams in the U.S. frequently have to step into roles traditionally held by product managers, needing extensive product knowledge to meaningfully contribute to business decisions.
Is Data PM the Missing Piece of the Puzzle?
As data scientists ourselves, it's unsettling to imagine a future where data teams are confined solely to technical execution—left out of the creative and strategic process of building meaningful data products. Yet that’s often the tradeoff in environments where the product function doesn’t truly understand data, and data professionals are left unsupported.
Without dedicated data-savvy product managers, we find ourselves stretched too thin—juggling engineering, strategy, user understanding, and long-term vision. We’re expected to do it all, while still delivering technical excellence.
So is it better to institutionalize the role of Data Product Manager? To draw a line between technical depth and product direction?
What do you think?
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