What Good Data Product Managers Do — And Why You Probably Need One

More and more of the data teams we talk to—and the organizations they support—are changing the way they treat their data assets. Instead of throwing a bunch of data into a database and trusting their analysts to manufacture gold, these teams are taking a decidedly more…operationalized approach to their data engineering.

They’re reviewing customer needs, facilitating adoption, investing in scalable tooling…

In short, they’re treating their data like a product.

But like any good product, data products can’t manage themselves. As organizations seek to sail their data practices into the modern data landscape, they’re increasingly looking to data product managers to guide the ship.

Whether they’re migrating to Snowflake, integrating with Databricks, moving towards a data mesh, or finally investing in their data quality, data teams are doing everything they can to better realize the promise of a “data-driven” company.

As an essential role of the modern data team, data product managers are responsible for identifying holes in the internal user’s data experience and then working with the data and analytics teams to bridge the gap. They drive the prioritization of projects and overall vision to optimize an organization’s internal capacity and enable consumers to effectively operationalize their data.

According to Wendy Turner-Williams, the Chief Data Officer at Tableau, any good data product needs a good data product manager. 

“From my perspective, a data product manager is actually one of the first roles I would hire for. I like them to actually create the vision and then drive the engineers to that vision,” said Wendy during a recent conversation. “For me, it is a critical component as I tend to focus on data product managers who can create a story, engage with our internal customers or even our product team.”

In this article, we’ll walk through:

What is a data product manager?

A data product manager (DPM) is a professional responsible for guiding the development and use of data-centric products within an organization. Bridging the gap between data science and product management, a DPM oversees the lifecycle of data products, ensuring that they align with business goals, provide value to users, and are built upon reliable and scalable data infrastructure. They work closely with data scientists, engineers, and stakeholders to define requirements, prioritize features, and ensure that the data product meets both user needs and business objectives. Essentially, they help translate complex data capabilities into tangible business value.

How did data product management evolve?

In the early 2000s, companies like LinkedIn, Netflix, and Uber had a problem. Teams across the organization were working with data, and lots of it, at scale. 

Data was powering their product roadmap, fueling executive-level decision making, and informing their paid marketing campaigns. 

Internal and external data was flowing in and out of the company. There were regulations, guidelines, and restrictions for how this data could be used and by whom. But nobody was in charge of developing data solutions to make analytics operational, scalable, and accessible. 

As a result, the product data manager role was created to answer questions like: 

• What data exists? 
• Who needs this data? 
• Where is this data flowing to/from? 
• What purpose does this data serve? 
• Is there a way to make it easier to work with/access this data? 
• Is this data compliant and/or actionable? 
• How can we make data useful to more people at the company, faster? 

What is a data product?

Of course, you can’t have a data product manager without a data product—or several.

But defining a data product is surprisingly difficult. The truth is, many things can be considered a data product, from a Looker dashboard or Tableau report, to an A/B testing platform or even a multi-layered data platform. 

Eric Weber, the Head of Data Experimentation at Yelp, suggests, “talking about data products in a generic way can produce generic results. Data product is a useful idea, but to make it really create value, we have to get into the specifics….” 

So let’s get specific. Regardless of what data the product visualizes / crunches / puts to work, there are specific outcomes it should deliver: 

• Increased data accessibility (surface data where people need it when they need it)
• Increased data democratization (make it easier for people to manipulate the data) 
• Faster ROI on data (quicker insights)
• Time savings for the data team / data consumers
• More precise insights (i.e., experimentation platforms)

Similarly, there are important characteristics or qualities a data product should have. 

• Reliability and Observability. Acceptable downtime for a SaaS product is a discussion of “how many 9s?” As in 99.9% or 99.999% availability. Just as software engineers use products such as Datadog or New Relic to track SaaS product performance, data product managers need solutions to identify and solve data product performance issues in near real-time. 
• Scalability. The data product should scale elasticity as the organization and demand grows.
• Extensibility. While the data product has likely been built from an integration of different solutions, it needs to maintain the ability to easily integrate with APIs and be versatile enough to be ingested in all the different ways end users like to consume data.
• Usability. Great SaaS products focus on providing a great user experience. They are easy to learn, fun to use, and quick to get work done. 
• Security and Compliance. Data leaks are costly and painful, as are regulatory fines.
• Release Discipline and Roadmap. SaaS products continually evolve and improve. Roadmaps are built at least a year into the future with a strong quality assurance process for updates.

What does a data product manager do?

A data product manager is a specialized role that sits at the intersection of data, technology, and business, but this clinical description barely captures what has become one of the most influential positions in modern enterprises. A data product manager is responsible for data democratization and increasing the time to value for the data itself. They design, build and manage the cross-functional development of a data platform, or a suite of specific data tools, to serve multiple customers across an organization. In essence, they transform raw information into strategic assets that companies can actually use.

The real-world impact becomes clear through examples like Atul’s work at Uber, where he defined the product strategy and direction for the company’s data analytics, data knowledge, and data science platforms. In his role, he led a project to improve the organization’s data science workbench that was utilized by data scientists to make it easier to collaborate. The challenge arose when data scientists were automating the process of validating and verifying worker documents that were required when applying to join the Uber platform. This was a great project for machine and deep learning, but the problem was data scientists would routinely hit limits of the available compute power, creating bottlenecks that threatened the entire initiative.

Whereas a traditional engineering project lead may have tried to add more virtual machines or extend the project timeline, Atul researched multiple solutions and identified virtual GPUs, then an emerging technology, as a possible solution. While there was a high price tag, Atul justified the expenditure with leadership by demonstrating that the project was not only going to save the company millions, but supported a key competitive differentiator. This proactive approach allowed Uber to start building the foundation they would need to leverage GPUs immediately upon availability. Time to value was greatly accelerated, representing what industry experts consider a hallmark of a good data product manager. The role requires professionals who can see around technological corners, making strategic investments in infrastructure that may seem expensive today but prove essential tomorrow.

What background do data product managers need? Who do they report to?

The path to becoming a data product manager rarely follows a straight line, reflecting the interdisciplinary nature of a role that demands both technical depth and business acumen. While you don’t need to write code, this is a difficult job to do without technical training. This is a role that requires understanding complex technical architectures and working with very technical colleagues who speak in algorithms, database schemas, and computational constraints. The technical bar for entry often surprises candidates coming from traditional product management roles, where familiarity with user experience design and market research might suffice.

The most successful candidates often come from backgrounds that have already forced them to navigate these technical waters. Common data product manager backgrounds include back-end engineering managers or strong engineers who want to set a broader vision beyond pure implementation, traditional B2B product management professionals seeking more technical challenges, internal tooling product managers who understand the pain points of building for developer audiences, and data analysts who have experienced firsthand the frustrations of working with imperfect data infrastructure. Each of these career paths provides different but valuable perspectives on the intersection of technology and business strategy.

What distinguishes the best data product managers, industry professionals say, is often their ability to serve as translators between highly technical teams and business stakeholders who need insights but may struggle with the underlying complexity. It can also be helpful if the candidate has experience talking to customers, as this can indicate they are skilled in translating requirements and telling stories to diverse audiences. This storytelling ability becomes essential when justifying expensive infrastructure investments to executives or explaining why a particular data pipeline redesign will ultimately save the company money, even if the immediate benefits aren’t visible to non-technical decision makers.

The reporting structure for data product managers varies significantly depending on company size and organizational priorities, creating what some describe as one of the most fluid hierarchical relationships in modern tech companies. Some data product managers are beholden to data analysts and data scientists, working directly within data teams to improve their tools and workflows. Others work with operations teams, software engineers, or in the case of larger companies, executives who view data infrastructure as a strategic differentiator. However the reporting is structured, the data product manager makes it easier for data consumers to understand and democratize not so much the data itself, but the insights gleaned from that data. This focus on insight accessibility, rather than raw data management, represents a fundamental shift in how companies think about their information assets and the people responsible for unlocking their value.

How much do data product managers make?

Data product managers are commanding increasingly competitive salaries as organizations scramble to harness the value of their information assets. According to recent industry data, these roles typically earn between $117,599 and $128,500 annually, though compensation can range from as low as $87,000 to as high as $197,000 depending on experience, location, and company size. The highest-paying states for data product managers are California, Washington, and Oregon, with San Jose leading at $161,267, reflecting the intense competition for talent in major tech hubs where data-driven decision making has become fundamental to business operations.The salary surge reflects broader market dynamics reshaping how companies value data expertise. Startups offer the highest average compensation at $140,338, as these companies often rely heavily on data products to gain competitive advantages and attract investors. Companies like Credit Karma and Meta are among the highest-paying employers for these roles, underscoring how tech giants are willing to pay premium wages to secure professionals who can transform raw data into profitable products. The broader product management field has seen over 26,000 job postings per week on LinkedIn in the US alone, and industry experts note that AI and machine learning skills command significant salary premiums. This trend particularly benefits data product managers whose work often intersects with artificial intelligence applications. This combination of high demand and specialized skill requirements suggests that compensation for data product managers will likely continue its upward trajectory as businesses increasingly recognize data as a strategic asset rather than merely a byproduct of operations.

Data product manager vs. product manager

The distinction between product managers and data product managers reflects the broader evolution of how organizations approach their most valuable digital assets. As companies have transformed from simply collecting data to treating it as a strategic product in its own right, new specialized roles have emerged to bridge the gap between traditional product management and data science.

A product manager (PM) is responsible for product success. They are the people who identify customer needs and then develop a strategy and roadmap for product development, shepherding products from conception through market launch and beyond. These professionals live at the intersection of customer feedback, competitive analysis, and revenue optimization. A data product manager is the data science version of a normal product manager. They are responsible for managing data and data flow throughout the product life cycle. Yet this seemingly simple distinction masks a fundamental shift in perspective.

Where traditional product managers build for external customers, data product managers serve a different constituency entirely. To a data PM, data is the product, requiring them to think about accessibility, quality, and usability in ways that have little to do with user interfaces or market positioning. Instead of analyzing customer acquisition costs or conversion funnels, they focus on data lineage, system uptime, and the speed at which analysts can extract meaningful insights from company databases.

This internal focus demands a dramatically different skill set. Traditional product managers, on the other hand, will make sure that the user journeys are well-developed and optimized. They pay attention to the way users interact with their features and can easily navigate through the application. Data product managers, by contrast, spend their days writing SQL queries and designing data architectures. Data product managers can translate business needs into SQL queries and come up with valuable ideas for improving your product and delighting your customers, bridging the technical gap that traditional product managers typically delegate to engineering teams.

The success metrics tell the story of two fundamentally different value propositions. Traditional product managers live and die by customer-facing numbers: user growth, retention rates, and revenue per customer. Their wins are visible in quarterly earnings calls and product launch announcements. Data product managers operate in a more subtle realm, where success means data democratization and increasing the time to value for the data itself. Their victories might be measured in reduced time-to-insight for business analysts or improved data quality scores that never make it into public presentations but quietly power better decision-making across the organization. 

Data product manager vs. data scientist

At companies across Silicon Valley and beyond, the distinction between data scientists and data product managers often confuses executives trying to build their analytics teams. Both roles work with data, both require technical sophistication, and both promise to unlock insights that drive business value. Yet their fundamental approaches to information represent different philosophies about how companies should extract value from their digital assets.

The main difference between these two roles is data scientists are trying to glean insights within an existing product or solution. When faced with a business challenge like declining user signups, a data scientist dives into the numbers to answer questions such as “why is a user not signing up?” They analyze user behavior patterns, identify correlation factors, and build models that explain what drives customer decisions. Their work tends to be diagnostic and predictive, focused on understanding phenomena that have already occurred or forecasting what might happen next.

On the other hand, a data product manager works to empower engineers, business stakeholders, and executive leadership by discovering “what is the best outcome for this data and how do we get there?” Rather than analyzing existing problems, they think strategically about untapped opportunities and infrastructure improvements. Consider Uber’s wealth of trip data collected every time a user requests a ride. A data scientist working with this information might help predict price points where users might complain or switch to another rideshare app, analyzing the factors that drive customer dissatisfaction with surge pricing.

The data product manager would approach the same dataset with entirely different questions, focused on what else can be done with the data, what other information it can be combined with, how to ensure the data pipeline remains reliable at scale, and whether the machine learning models powering various features are adequate for future needs. They might envision combining trip data with weather patterns, local events, and traffic information to create entirely new product capabilities, then build the infrastructure and team processes needed to make those innovations possible. While the data scientist asks what the data means, the data product manager asks what the data could become.

Data product manager vs. data analyst

Walk into any modern corporate office and you’ll likely encounter both data analysts and data product managers, yet many executives struggle to articulate the difference between these two roles beyond the vague sense that both “work with data.” The confusion is understandable given how closely these professionals collaborate, but their relationship resembles that between a restaurant critic and the farmer who grows the ingredients.

A data analyst is a practitioner of the data, leveraging existing data products to uncover insights and derive value from data assets. These professionals spend their days writing SQL queries, building dashboards, and creating reports that help marketing teams understand customer behavior or operations managers track efficiency metrics. The data analyst isn’t responsible for creating new data products but serves as one of the many internal consumers of existing data products, relying on clean datasets and reliable tools to generate the insights that drive business decisions.

A data product manager operates at an entirely different altitude, focused not on extracting insights but on building the infrastructure that makes such extraction possible in the first place. A data product manager is the role responsible for understanding the needs of data consumers like data analysts, and then developing and maintaining the data product in order to meet those specified needs while also uncovering new opportunities to leverage data for stakeholders.

Consider how each role might approach a company’s desire to reduce customer churn. The analyst dives into historical customer data, identifies patterns in cancellation behavior, and builds models predicting which customers might leave next. Meanwhile, the data product manager ensures the analyst can access clean customer data without waiting weeks for IT support, that analysis tools run fast enough for real-time insights, and that predictive models can integrate seamlessly into customer service workflows. One extracts meaning from information, while the other builds the foundation that makes meaningful extraction possible.

The future of the data product manager

Data teams are becoming increasingly decentralized and splintered – there are more roles emerging, from data governance managers to analytics engineers. 

At the same time, the distance between data producers and data users is growing and demand is increasing exponentially. This is due in part to the growing reliance on data across all parts of an organization. 

The future of the data product manager will very much resemble the traditional product manager: a conductor that spans silos and inspires teams to play in harmony. 

They will be the critical connection point between data team members, data consumers, and product builders. Data product management will bridge the divide between data product and data as a service. They will identify the needs of users, monitor developments, evangelize a vision, coordinate stakeholders, and prioritize projects.

As a result, the organization will move from a reactive posture of fighting data fires to a proactive stance of building internal data capabilities as a competitive advantage.

Progressive data product managers will critically examine the qualities of a good data product and set their own metrics (we have some suggestions for downtime and data quality). 

Data product user satisfaction will be surveyed, downtime measured, and release processes documented. It will all be tied back to business value and evangelized across the company.

And that is an exciting future for data professionals indeed.

Connect with Barr and Atul on LinkedIn.

Are you developing and managing the data products for your organization? Book a time below to see how Monte Carlo can help take your data quality to the next level.

Our promise: we will show you the product.