So You Want to be a Data Product Manager: How Technical Do You Need to Get?
You look at the data product manager job ad, thinking “this sounds really interesting” but the words ML, AI and statistics seem to permeate the page. Is this a data scientist or ml engineering role in disguise? Is this a role where you’re expected to be everything, including a product manager? Does the company really know what they are looking for? These concerns are real and are distilled into an issue I hear about from many of you: “How technical do I need to be to be a data product manager?”
Take a step back and think about what it means to be a great product manager. You need to understand and represent your customer, navigate the role of your product within the company and ensure you understand the broader market opportunities to navigate build vs. buy conversations. These elements remain critical in the data product space - there is not something magical about this domain. So I turn this question back to you: how technical do you need to be to feel comfortable with those responsibilities?
Let’s use experimentation as an example. I am fortunate to lead an outstanding group of experimentation product managers. Each of them have varying levels of background in experimentation. It is not necessary to be a world expert in experimentation and causal inference to create an effective data product. But what each person can do is to works with experts, understand the broader product landscape in tech and elsewhere, look at where the company is going and translate those needs into an effective set of requirements for the experimentation product.
The more I work with this awesome product management team, the less I think that “how technical do you need to be?” is even the right question. Instead, I suggest that data product managers ask themselves or their company, the following questions to replace that larger question:
Who are my customers? If you read this newsletter regularly you recognize this as a question I ask all the time. I’ll say it again. You must understand who your customers are because if you don’t, you have no chance at answering the critical question of what are they trying to do? Your level of technical sophistication and background needs to be sufficient to understand what your customers are trying to do and how that experience could potentially change and improve.
Who are the technical experts internally? Technical expertise must come from somewhere. This need not always be the same place. I’ve seen technical leadership come from product, from engineering and from data science. Some companies actually define who owns the technical aspects of a product! If you get an answer that sounds like “uhhhhhh” when you ask about technical leaders, you can be sure part of your job will be to fill that gap.
What does it take to keep up with trends in the industry? Data products are changing incredibly fast. As a product manager in this area, your responsibility includes understanding how other companies and products are innovating to meet the unique demands of business. This sometimes involves reading papers, going to conferences, engaging with technical experts and keeping tabs on best practices across companies. It is not easy to do.
Am I managing this product alone, or am I part of a team? One benefit of working on a large internal product is that you are likely to not be solo as a product manager. You don’t have to be “everything”. But the reality is for most products people still view the 1 PM to 1 product mapping as the default. If you find a place where you can be part of a team, you don’t need to be strong in every place.
How much time and space am I provided to learn more about this space? While most companies would love to hire someone ready to hit the ground running, the competitive pressures of the market and the constraints on salary and compensation mean they can’t just go pick who they want. This creates opportunities! But companies must commit to the time and space needed for newer data product managers to learn and onboard in their space.
While these questions are likely the start of a larger conversation, I promise that asking them will provide more insight and actionable feedback than asking “how technical do I need to be?” Technical ability is hard to quantify and even harder to evaluate in these type of product roles. Next time you come across a compelling job ad or want to create a role internally, don’t focus heavily on the “ML”, “AI” words all over the page. Most likely, the company isn’t quite sure what they want either.