Updated: March 26, 2025- 13 min read
AI and machine learning are driving strategic discussions in businesses, from boardrooms to casual meetings. But many companies fail to fully leverage the data behind the trends, like buying the latest iPhone, but only using it to check emails.
The companies that thrive focus on data, organizing and using it effectively. These are the ones that hire Data Product Managers.
Data Product Managers are different from other product management roles. They help conceptualize data infrastructure, analyze data, identify users, and build data products. In this article, we'll explain how Data PMs differ, why companies need them, their responsibilities, and how to become one.
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What Is a Data Product Manager?
Data Product Managers play a crucial role in guiding the development of products using data analytics and data science techniques. The consensus in data product management is that data product managers need both business skills and knowledge of data and analytics.
A good Data Product Manager focuses specifically on leveraging data to drive business decisions and strategies. They bridge the gap between technology, design, and business, ensuring that data is used effectively to meet organizational goals.
Unlike other many PMs, Data PMs don't always focus on creating data products for external customers. Instead, the Data Product Manager’s job is to solve problems. Whether these are related to commercial products or internal operations is up to the employer. More often than not, their main goal is to use data to improve processes within the company, not just to cater to the end user.
Communication is another forte of any good Data PM. Aside from figuring out solutions, they collaborate with cross-functional teams to align data initiatives with the broader vision.
Data Product Manager Job Description
When looking for a Data Product Manager, companies typically seek candidates with a mix of technical expertise, analytical skills, and business acumen. The following requirements are most commonly found in job postings for this role:
Background in Data Science, Data Engineering, Data Analysis, or Product Management
Solid documentation writing skills
Strong verbal and stakeholder-facing communication skillset
Solid understanding of the data industry, including trends, tools, best practices, and functionalities
Knowledge of Agile methodologies and experience working in Agile environments
Ability to communicate data insights and product updates to stakeholders and executive teams.
Experience across the following technologies:
ETL processes, Analytics, and Integrations
Proficient in either SQL, Python, Javascript/NodeJS
Experience with Database Design & Administration
Proficiency with data visualization tools like Tableau, Power BI
Strong problem-solving skills and ability to think analytically
Experience in managing projects, including setting timelines, milestones, and deliverables
Strong business acumen and understanding of how data can drive business decisions
Data Product Managers are Responsible for:
Defining product data goals that align with key business objectives
Using data analyses to specify new data products and features
Building frameworks to set and track OKRs and KPIs throughout the data product lifecycle
Building a product roadmap for data-driven products
Translating large data projects into actionable tasks
Promoting data literacy and increasing adoption rates within the organization
Developing new data products in line with the product strategy
Managing data science and engineering to enhance product experiences
Managing the development of a data platform
Analyzing data to identify trends that inform product decisions
Managing data sets to ensure reliability and quality
Building and maintaining the data infrastructure for optimal performance
Monitoring and evaluating the data product performance
Working with data to develop metrics to measure product success
Informing stakeholders on state-of-the-art
Data Product Managers are not responsible for:
Conducting in-depth market analysis and competitive research to identify opportunities and threats (Product Specialists)
Setting the overall vision and long-term strategy for the commercial product (Product Managers)
Conducting interviews with customers and gathering detailed feedback to inform product development (Product Managers or Research Specialists)
Developing marketing strategies, creating go-to-market plans, and coordinating with sales teams (Marketing Specialists)
Managing the product's profit and loss, pricing strategy, and overall financial performance (Product Managers)
Providing specialized support and training to customers on product features and functionalities (Product or Support Specialists)
Handling specific technical issues and queries from customers (Technical Support)
Designing and maintaining data pipelines to ensure efficient data processing and transformation (Chief Data Officers or Data Engineers)
Managing data storage solutions, ensuring data is stored securely and efficiently (Data Engineers)
Developing ETL processes to integrate data from various sources (Data Engineers)
Working with the data to ensure quality, integrity, and reliability (Data Engineers)
Optimizing system performance for data processing (Data Engineers)
Developing machine learning models with the available data (AI Engineers)
Writing code and developing software features (Software Developers)
Managing IT infrastructure, servers, and network resources (System Developers)
Designing the detailed user experience and user interface (Usability Specialists)
Why Do Companies Need Data Product Managers?
While most organizations are trying to go digital, only a few are doing it right. In fact, McKinsey reports that 70% of digital transformation projects fall short of their goals. So, what’s the problem?
Technology is rarely the problem; the main challenge lies in overcoming 'people challenges'. Precisely, leadership's inability to convince employees to embrace data-driven decision-making is a problem. A survey by NewVantage Partners found that 48% of executives see "people challenges" as the main obstacle to becoming data-driven, while only 19% blame "technology."
Data Product Managers are the key to making companies data-driven. The Data Product Manager’s role involves crafting a data strategy based on available company data. They are the ones with a comprehensive overview who can ask each department, "What business questions are you trying to answer?" and "What goals are you trying to achieve?"
Once they collect the answers, they orchestrate the data and serve it.
For example, the marketing team might want to use data science to analyze customer purchase patterns to improve targeting. The IT team may need data analysis and analytics to prevent downtime. Effective data product management ensures each team can leverage data to answer critical business questions.
Additionally, having a Data Product Manager can help companies centralize data management. Data professionals keep data sets and insights in one place. This reduces gaps between teams and relieves individual contributors from managing large-scale data. Just imagine how this helps everyone focus on their primary tasks.
The evolution of data product management
Data product management has transformed significantly in recent years, evolving from basic data storage solutions to sophisticated systems that drive strategic business decisions. A data product mindset requires a different approach to data management, fundamentally changing how organizations view their data assets. Rather than treating data as a passive resource, this mindset conceptualizes data development as projects designed to serve specific organizational purposes, including:
Identifying customer churn
Tracking annual recurring revenue
Managing inventory
This paradigm shift encourages data teams to proactively seek product opportunities that deliver tangible value and align with strategic objectives, moving away from merely chasing metrics or reactively responding to requests. By focusing on meaningful projects, organizations activate their data assets and technical expertise, resulting in data products that form part of a larger data web. Of course, these projects require Data PMs to act as a bridge between data teams and leadership and find ways to bridge data usability gaps within the organization.
Future trends for Data PMs to watch
AI data analytics: AI-powered tools will increasingly assist product managers in analyzing vast amounts of data, identifying meaningful patterns, and even predicting the success or failure of a product before its launch. This technological advancement is expected to dramatically impact the data management landscape.
Data democratization: Data democratization involves making data more accessible to non-technical users through user-friendly tools and platforms, enabling them to access, analyze, and interpret data without specialized expertise. Experts predict that by the end of this year, 75% of large enterprises will be using at least four low-code development tools for both IT application development and citizen development initiatives.
Personalization: Hyper-personalization driven by sophisticated data collection and analysis tools will transform how companies engage with customers. Businesses increasingly rely on real-time data to deliver personalized experiences tailored to the unique preferences of individual users. This trend will require Data Product Managers to leverage customer data more effectively than ever before, using advanced analytics and behavior-tracking tools.
Advanced collaboration and interoperability: The future of data product management will see significant changes in how teams collaborate and how organizations structure their data functions. The adoption of hybrid and multi-cloud strategies highlights how organizations are restructuring their data infrastructure to maximize flexibility and capability.
Average Data Product Manager Salary in the US
Here's some detailed data, beyond LinkedIn averages.
In the United States, the average salary for a Data Product Manager is $156,482 per year, which is about $75.23 per hour. Entry-level positions start at $130,328 annually, while experienced professionals can earn up to $197,979 per year.
Needless to say the numbers add up. Given the scarcity of talent and more high-profile companies seeking these specialists, the higher salary range is well justified. In comparison, Product Manager positions have lower average salaries, around $95,822 per year, which is significantly less.
Data Product Managers vs Product Managers
Not all product managers are data product managers. In fact, they rarely are. They may be data-driven, of course. However, their job title and key responsibilities are quite different from one another.
Traditional product managers are in charge of developing products that meet user needs. They have experience in product management, focusing on features, user feedback, and market trends.
Data Product Managers (Data PMs) are a bit different. They need to work closely with data. They are more likely to wield data science than to be know-it-alls of common product management jargon.
They use data to develop data solutions and make sure the product meets user needs. Data PMs also handle data governance, ensuring data is accurate and secure. While traditional product managers focus, well, on products, data product managers focus on leveraging the data.
Data Product Manager vs. Data Analyst vs. Data Scientist & Other Data Roles
As organizations increasingly rely on data-driven decisions, the variety of data-based roles has evolved, giving rise to several specialized positions. These roles ensure that data products are developed, managed, and utilized effectively.
Data Product Partner: Works closely with stakeholders to ensure data products align with business goals.
Data Product Owner: Compared to data PMs, they tend to have a more tactical role. They translate high-level strategy into actionable tasks and execute them.
Data Analyst: Analyzes data to provide insights for data products. They are focused more on data analysis and reporting in general.
Data Engineer: Builds and maintains the infrastructure and systems for data products. Goes more in-depth to implement data architecture.
Data Scientist: Develops advanced analytics and machine learning models for data products and other business purposes.
How to Become a Data Product Manager
Becoming a Data Product Manager is an exciting career path that merges data science and product management. As you've seen, it requires a mix of analytical, technical, and business skills. By developing these areas, gaining relevant experience, and continuously learning, you can successfully transition into this rewarding role. Here are the best practices to guide you on this journey.
1. Gain Experience in Product Management
Experience in product management, regardless of the technical acumen most companies say you need first, will help you. Defining product requirements, creating roadmaps, and managing lifecycles can help you understand the product and business side of things.
Therefore, start by getting certified for a PM role by recognized industry names. You'll learn about the essentials and get closer to securing your first job.
Furthermore, knowledge of Agile methodologies aids in the cross-functional development of a data product. It's how products are built in most companies; understanding the methodology will help you collaborate better. It is critical in companies where product managers create data-driven solutions.
2. Learn About Data Governance
Data governance involves ensuring data quality, security, and privacy. Learning best practices will help you down the line. You’ll be well-equipped to implement policies that keep data accurate and secure.
To learn data governance, start simple. Read books and articles about it. You’ll gain a high-level overview and a better understanding whether it’s a good calling for you.
Then, take online courses that teach the basics of managing and protecting data. Practice by organizing data at school or in simple projects. Ask your mentors and superiors for help if you have questions. Joining a club or group focused on data science and governance can also be useful.
This knowledge is essential for managing data collection and integrity.
1. Build Data and Technical Skills
One thing we hope we were clear enough by now — technical background is a significant advantage! Learn about database management, ETL processes, and big data technologies like Hadoop and Spark.
For your journey to run as smoothly as possible, you need to be comfortable with data. Therefore, learning data analysis tools like SQL, Python, and R will come in handy. Get familiar with data visualization tools like Tableau or Power BI. Practice analyzing real data to identify trends and insights. Understanding statistical methods will also be beneficial.
Understanding how data infrastructure works helps in collaborating with data team members like data engineers and scientists.
3. Understand the Business Context
Data Product Managers need to align data products with business goals. Develop strong business acumen by learning about market research, competitive analysis, and customer needs. Understand how to use data to build solutions that meet business objectives and create value.
Bear one thing in mind though. The best way to learn business is by doing it. You need to prioritize getting real-world experience. Sure, read about business, economics, and philosophy to understand the fundamentals. However practical experience combined with deep learning will give you the best insights.
4. Start with Internships or Entry-Level Roles
If you're just starting, look for internships or entry-level roles in data analysis or product management. These positions provide practical experience and help you build the necessary skills and knowledge for the position of Data Product Manager.
Visit websites like LinkedIn and Glassdoor and look for companies hiring junior talent. It should be a good enough starting point. If you feel like your portfolio is not as rich enough then take matters into your own hands. Gain practical experience by working on data projects, participating in hackathons, or volunteering in relevant roles.
5. Speaking of Networking...
Networking can open up opportunities and provide valuable insights. Start by joining professional groups related to data product management. Attend industry events, both in-person and virtual, to meet experts and peers.
On platforms like LinkedIn, connect with Data Product Managers and actively engage with their posts. Don’t just ask for advice; share your insights and experiences too. Participate in discussions, webinars, and online forums to stay updated and visible in the community. Building genuine relationships can lead to mentorship opportunities, job referrals, and valuable career guidance.
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Go, Seek Your First Data Product Manager Role
A unique blend of technical appeal, business acumen, and product management — it’s what Data PMs are all about. By honing technical skills and gaining real-world experience, anyone can excel in this rewarding role. Plus, companies will love and reward you for it.
Therefore, start immediately. Seek job opportunities. Focus on continuous learning and networking to stay ahead in this evolving field. Start by exploring our resources and start building your skills today.
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Updated: March 26, 2025
