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You need more than technical skills to build a great data career

Allie Beazell is director of developer marketing @ Census. She loves getting to connect with data practitioners about their day-to-day work, helping technical folks communicate their expertise through writing, and bringing people together to learn from each other.

If you’re trying to land a data career by focusing solely on technical data skills, you’re going to have a bad time. Here’s an important truth: A data career is just as much a people-focused job as it is a data-focused job.

The importance of so-called soft skills was one of the main focuses in our discussion about data careers during the fourth session of our Operational Analytics Conference. On our panel, we featured some folks who do a lot of thinking about which data experts to hire:

The board agreed that to land and build a career in data, you need to understand what data leaders look for in candidates today. Turns out, much of what they look for has little to do with the technical aspects of data and more to do with the people data helps and the problems it solves.

Learn how to work with people and to understand what they need

A common refrain in our discussion was that you can teach the technical nuances that come with working in data. Teaching soft skills—like managing expectations, knowing (and clearly communicating) how long a task might take, and understanding why you’re doing that task in the first place—isn’t as easy. So, to land that awesome data job, you need to demonstrate you’ve already developed these soft skills or are actively working on them.

“The hardest part of data is not actually writing code,” Rachel said. “The much harder part of data is dealing with people. So, we’re looking for soft skills and then are willing to teach technical skills.”

Sarah echoed this point by saying that “technical skills are much easier to teach than business context.”Business context is how the business uses data, and it’s vital that you know how your work fits into larger organizational objectives for the teams you support. If, for example, you’re segmenting audiences for the marketing team, the business context is better audience targeting for a more effective ad campaign.

Familiarity with how stakeholders use data quickly emerged as the most important trait, and it’s only possible to gain via second-hand gold or hard-won experience. If you don’t want to spend years running ad campaigns for the sake of context, the next best thing is to tap into the experience and knowledge of your coworkers who design those campaigns daily.  

“What I think made the data teams that I’ve worked on be successful is an understanding of what people are doing with the data,” Rachel said. “Then, I can build tools and systems based on how I know it’s going to be used. If you’re an analytics engineer, data analyst, or a data product manager, you need to understand why marketing is asking this question.”

So, what does this mean for a hypothetical interview? Be curious and easy to talk to. Don’t rely just on your data or coding skills; exhibit an understanding of how data impacts business.  If you have gaps in your knowledge, show a willingness to learn. All of this together proves to your potential employer that you’re easy—and possibly fun—to work with.

Spend time and be active in data communities

When asked about where they find new data hires, the panelists all pointed to data-focused communities and content. Participating in data communities and writing data content can start to build a professional network of data professionals that think the same way you do.

Every panelist mentioned two communities as a good source for data hires: the dbt Slack and Locally Optimistic. Emilie said that Netlify made five data job offers in September and December of 2020 and that “three to four of them came in through the dbt and Locally Optimistic communities. Those were really great because there’s a baseline level of like, ‘Oh, we’re thinking about data in similarish ways.’” Garegin also said that Fivetran has had a lot of luck with both communities.

Emilie also mentioned Outer Join, a data-focused job board, as a good source for data talent. Lastly, when it comes to senior-level roles, personal blogs are a huge plus. Joey also chimed in to mention that, especially for experienced and high-level roles, having a data-focused blog is a huge plus.

The overarching theme here is that the people hiring for data roles are active members of data communities. They participate in the same discussions you should participate in, and they read the same blog posts you read. So, actively participate in data communities and even produce your own content if you’re so inclined. Data job offers may start to land in your lap if you can build a following among other folks who love data.

Aim for either depth or breadth of knowledge

Every panelist said there were two tracks at their company: one for individual contributors and one for management. You won’t be stuck on one track throughout your career—it’s not uncommon for people to switch between tracks pretty often, the panelists said—but having an awareness of them will help you create the career you want.

Each track favors a different type of knowledge:

  • To be a high-level individual contributor, you need deep knowledge in your area of expertise.
  • To be a high-level manager, you need a broader perspective about data and how it impacts the business at large.

For individual contributors, Rachel used an anecdote based on the alphabet. If you’re given a task, how far you make that task down the alphabet without handholding denotes your seniority. So, a junior engineer may take a task from A to C before looping someone in for help, review, or approval. A senior engineer, on the other hand, can take a task from A to Z before looping someone in due to their expertise and the trust they’ve built.

A manager needs a broader skillset focused on securing buy-in for data initiatives and then using that buy-in to build systems, like operational analytics. Sarah put it this way:

“You’re not just solving a one-off problem. Instead, over time as you see these problems arise, you should be able to identify patterns of problems. What are the systems that you need to build in order to solve for these patterns of problems? That translates into an architectural way of thinking around not just writing a query to solve something, but actually thinking about the bigger soul of that problem or type of problem on an ongoing basis.”

No matter which track you chose, as Rachel says, “The more senior you are, the more proactive you should be.” As a high-level individual contributor, you’ll know (to follow Rachel’s metaphor) what it takes to get from A to Z and can plan accordingly, manage expectations, and possibly get ahead. As a high-level manager, you can foresee patterns of problems, pivoting systems and strategy to compensate for next month’s problems today.

In an interview setting, showcase this proactivity by not only understanding what’s required of someone at a high level in the position you’ve applied for but also the impact your decisions might have. As a data analyst, can you get ahead of any requests marketing may have without being prompted? As a data lead, can you secure buy-in for a slick new tool your analytics engineers have been asking for by creating a detailed implementation plan?

Future data leaders need to understand the future of data operations

Of course, in addition to all of this advice, to be a great data professional, you need to stay on top of data industry trends. One of the most important data trends of late is an emerging class of data tools and processes that are changing how organizations use data. At Census, these kinds of trends fascinate us endlessly, so subscribe to our podcast and newsletter to receive the latest insights. We can help you not just build the future of your data career but also the future of data as a whole.

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