The How to Get an Analytics job podcast sat down with John Pauler, Chief Growth Officer of Maven Analytics, to talk about his 7 Step Roadmap for Landing a Data Job in 2022.
The first step is carving out a general plan for what your career goals are. In this plan, you should include what the basic steps are for your journey along with a timeline of when you want to accomplish these steps. John Pauler emphasizes “ don’t spend more than an hour on this, we as analytical people we tend to overthink things”. For this first step think about it as a rough draft where you are simply laying out a logical progression that you can follow to start learning about the roles you want and how to acquire skills to be going generally in the right direction. The reason you don’t want to get hyper-fixated on this plan is that along the way you’re likely going to pivot, and your plan will be adjusted as you learn new things and meet new people. Don’t make the mistake of getting stuck just thinking about this plan forever. Give yourself an hour to put something down on paper and just start.
The second is understanding the data space and narrowing down jobs to what type of role you might want to get into. Deciding what type of role you want will guide the future steps in what you decide to study and what skills you will need. A real challenge is figuring out which tools to learn in practice, there are many tools in the data space including, Excel, SQL, Python, Tableau, Power BI, to name a few. However, it’s not worth mastering all of these when most positions require mastery over just two to three. That is why understanding which of the different types of roles you’re interested in is important when starting out your career in data. The most popular roles are a data scientist and data analyst, data engineering roles are also becoming popular. While there’s definitely overlap between those three in terms of some of the skills that you want to learn but there are also some slight differences that will shape how you prepare for landing these roles. The main point of step two is to understand the right tools for the specific role you want and prioritize those tools for your next step, the learning process.
Step three involves learning the actual tools and skills that you’re going to need in the specific role that you’ve chosen for your career. John David advises finding three of your ideal entry-level jobs, going down to the skills section in the job description, and using that as your blueprint for deciding what tools and skills to learn. For example, if you want to be a data scientist or a data engineer you will more than likely need python versus if you want to be a data analyst. For a data analyst, or a business intelligence analyst Python can be very valuable as a tool, and it has some great applications but if you are just starting out your learning journey Python would not be the first tool to learn for those roles. Higher on the list for those data analyst and business intelligence roles would be tools like SQL and Tableau or Power BI. In fact, SQL is John Pauler’s go-to tool for his current role at Maven Analytics and what he focuses on teaching to students on his platform. A lot of businesses and organizations use SQL for their data and having this skill set is very valuable across several types of roles in the data space. There are multiple paths you can take when learning data skills. These paths include your traditional college or university, Data Bootcamps, Online courses through Udemy, part-time programs like the one Maven Analytics offers, or apprenticeship programs like the one being offered through Greensboro College’s Business Analytics Certificate Program (https://www.greensboro.edu/academics/certification-innovative-programs/)
Step four is learning how to think like a data person. This is a step that Maven Analytics emphasizes. Although it is necessary to learn the technical skills that a role requires as mentioned in step three, it is just as important to learn how those skills and tools should be applied to solve business problems and bring value to a data team. This is learning to think in an analytical framework and applying critical thinking with rigor.
Step five is building a project portfolio. Having a portfolio that displays how you use the skills in your resume helps employers understand the value you bring to the table and what it might be like working with you on a project in the workspace. However, it’s not just for interview content and display, you really do need to have some hands-on experience with the tools you learn. Ideally, you should have some good robust projects that are nicely packaged up for presentation so you can walk through the project and point out the high-level business problem, the key insights, and explain any data visualizations. Putting together this portfolio should involve projects you are interested in and excited about to facilitate hooking the audience with an interesting problem that you are attempting to solve. When it comes to showcasing your portfolio don’t lead with the technical piece instead hook them with an interesting business problem then show the value in the insights and tell a story through data visualizations and then, finally, you flex your technical skills. Leave the technical vernacular for the end, it’s a much more effective way to showcase your skills and appeals to a much broader audience if you can talk about your projects that way.
Step six is about learning how to market yourself and learning how to network to land a role you want. When you market yourself, you need to frame your projects as real data experiences. If you are looking at entry-level positions, you probably already noticed that there are few compared to the amount of mid to senior-level roles open which makes them very competitive. In the job description and requirements for these entry-level roles, you have probably also seen the bullet point asking for 2+ years of experience. When marketing yourself you don’t want to lead by saying you don’t have any experience, this will likely eliminate you from the applicant pool. Instead, talk about your projects as experiences, although they are not actual workspace experiences, people want to know that you have the ability to perform the job they are hiring for. When entry-level job descriptions state they want a candidate with two years of experience a lot of times what they’re really looking for and care about is your ability to do the work. When it comes to networking you want to make sure you are connecting with people that are in the space you are interested in. Also, when connecting with these people think about it as a mutual relationship and give them a reason to be interested in a connection with you. One of the worst mistakes people make when networking is approaching people with a “how can you help me” and “what can I get from you” mentality. You need to show a genuine interest in connecting with and talking to these professionals, take the time to read about the work they do, and find some common ground so you can have a real conversation with substance.
Step seven, the final stage is getting really good at the analyst interview. When you interview you need to keep in mind that you are not getting the job just because you have the skill set, you’re getting the job because you can solve problems, the skillset comes secondary. A lot of people interviewing over-emphasize their technical skills and leave their softer skills in the background. This is often a huge mistake; John David likes to give the analogy of hiring someone to build a shed. If you are hiring a shed builder you don’t want to see a certification on how well they can swing a hammer or use a screwdriver, you want to see some evidence of the sheds they have built. They want to see your style and your overall effectiveness. If you can show these things to the person hiring you will at the same time show that you are great at using the tools required. These people hiring are looking for results and impact! There are lots of candidates that can use the tools, can extract data, can create beautiful visualizations but the people that really stand out and are extremely valuable can do all those things on top of also understanding the business problems. These MVPs know how to find valuable levers that can improve the business, they’re great at communicating, they’re great at rallying people, and great at advocating to take action-based approaches that drive value. You need to communicate that you can become this type of employee for the role you are applying for.