{"id":111336,"date":"2026-06-12T12:00:00","date_gmt":"2026-06-12T12:00:00","guid":{"rendered":"https:\/\/www.red-gate.com\/simple-talk\/?p=111336"},"modified":"2026-06-12T09:48:59","modified_gmt":"2026-06-12T09:48:59","slug":"from-anthropology-to-analytics-how-to-build-a-data-career-with-no-prior-experience-10-tips-from-a-microsoft-mvp","status":"publish","type":"post","link":"https:\/\/www.red-gate.com\/simple-talk\/opinion\/career\/from-anthropology-to-analytics-how-to-build-a-data-career-with-no-prior-experience-10-tips-from-a-microsoft-mvp\/","title":{"rendered":"From anthropology to analytics: how to build a data career with no prior experience (10 tips from a Microsoft MVP)"},"content":{"rendered":"\n

Kristyna Ferris didn’t come from a traditional tech background. She studied anthropology, stumbled into data through an admin job, taught herself Power BI on her lunch breaks, and eventually built a career that spans Microsoft Fabric, PySpark, Python, and cloud data architecture – plus a position on the Microsoft Most Valued Professionals (MVP)<\/a> program along the way. Oh, and a $20,000 saving on the grad school she didn’t attend…
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Kristyna’s story is less about having the right qualifications and more about making deliberate moves, learning with purpose, and showing up in the right communities. Whether you’re yet to start your first job or desperately want a career change, here are ten of the most practical lessons from Kristyna’s experience for anyone looking to grow in data.<\/strong><\/p>\n\n\n\n

This advice includes excerpts from Kristyna’s appearance on the Simple Talk podcast. You can listen or watch the full episode here.<\/a><\/em><\/p>\n\n\n\n

1. You *just*<\/em> need a foot in the door – and how you get there is up to you<\/h2>\n\n\n\n

Kristyna’s entry into data wasn’t planned – she was working as an admin assistant when her employer discovered she knew Microsoft Excel<\/a>. That alone quickly elevated her to taking ownership of the company’s logistics and, from there, everything followed.

The lesson isn’t that Excel is a superpower (though it helps!), but more that early-career opportunities rarely look like the job you actually want<\/em>. Quite often they, on paper, look like the tasks nobody wants to do. So, being enthusiastic and jumping onto the tasks – giving them you’re all – makes you stand out.<\/p>\n\n\n\n

The key learning here?<\/strong> If you’re trying to break into data, stop waiting for the perfect entry-level role. Look for adjacent work \u2014 reporting, operations, admin \u2014 where data skills are useful but not the primary requirement. Get in the building first (a foot in the door). <\/strong><\/p>\n\n\n\n

2. The best career moves are often the ones we don’t plan<\/h2>\n\n\n\n

While working in logistics, Kristyna built a Power BI<\/a> report to argue for a team transfer. She wanted to move to a different operations team. Instead, the report made its way up the chain and landed on the desk of the analytics head who – guess what – decided she should be on the analytics team!<\/p>\n\n\n\n

Kristyna wasn’t even trying <\/em>to get into analytics – she was just trying to solve a problem with the tools she had. But, the work spoke for itself, and a new door opened.

The key takeaway: don’t wait for a clear path to open before you do good work! Build things that show what you’re capable of, even if – and especially<\/em> if – the audience isn’t who you expected.<\/strong><\/p>\n\n\n\n

3. Consulting accelerates your learning faster than almost anything else<\/h2>\n\n\n\n

Perhaps this is a controversial take (and we’d love to hear your thoughts in the comments!) but, for Kristyna, consulting<\/a> helped her career enormously. It exposed her to more technology, more problems, and more industries than any single employer could have.

As she described in the episode: “I would have probably never learned PySpark<\/a>, Python<\/a>, in-depth SQL… I don’t get that experience unless I’m in consulting.”<\/em><\/p>\n\n\n\n

In just one year she gave talks on OLTP<\/a> to OLAP<\/a>, inheriting Power BI reports, Microsoft Fabric admin, licensing, and PySpark – all drawn from real client work. That breadth simply isn’t available in most in-house roles, where typically you’re tied to the stack your organization has already chosen.

Top tip: if you’re feeling stagnant in a technology role, consulting (even if it’s contract work) is worth considering. The variety of problems forces you to learn in ways that self-directed study rarely does.<\/strong><\/p>\n\n\n\n

4. Learn the tools that actually solve your problems – not the ones that sound impressive<\/h2>\n\n\n\n

When episode host Steve Jones<\/a> noted that C#<\/a> is a programming language many developers struggle with, Kristyna’s response was disarmingly honest: “I only learned it for Power BI – that’s the trick! I’m very bad at learning tools ‘just because’.”<\/em>

It may sound obvious, but it’s a useful career philosophy. The data world is full of people chasing certifications and frameworks they don’t have a real use for<\/em>. Kristyna’s approach is different: identify the friction in your current work, find the tool that removes it, and learn that.

The advice: before starting your next course or certification, ask yourself honestly – is there a real problem this solves for me right now?<\/em> Learning with a concrete application is dramatically faster – and more likely to stick – than learning in the abstract.<\/strong><\/p>\n\n\n\n

5. Getting rejected early<\/em> is not the same as getting rejected permanently<\/em><\/h2>\n\n\n\n

Kristyna’s first Microsoft MVP nomination was declined. She’d only given one talk, because that was the one she felt comfortable delivering. <\/p>\n\n\n\n

OK, it’s not exactly the same as a job rejection, but the point is the same: she was rejected but, crucially, didn’t let that stop her. She kept speaking, kept submitting, and was eventually nominated again – this time successfully. Kristyna now sees rejection as simply being part of the process.

Key advice: in community contribution, as in most careers, consistency beats a single impressive attempt. Submit the talk (or job application) even if you’re not sure it’ll be accepted. Give the talk even if the room is small. The body of work accumulates over time in ways that individual efforts don’t.<\/strong><\/p>\n\n\n\n

6. Knowing what people want<\/em> is harder than knowing what they don’t <\/em>want<\/h2>\n\n\n\n

This might be the most practically useful thing Kristyna said for anyone building reports<\/a>, dashboards, or data products of any kind.

As she said in the episode: “Nobody’s going to tell me what they want. They’re going to tell me what they don’t want. The faster I can get to failure, the better.”<\/em>

Kristyna’s approach is simple. Mock something up quickly – sometimes using AI to generate a draft dashboard – and put it in front of stakeholders. Maybe it’ll be right, maybe it’ll be wrong, but that’s not the point. The intention is to have something<\/em> to show people; humans are often more able to articulate their requirements once they can see something to react to.<\/em>

The takeaway: stop trying to gather perfect requirements before you build! Build something imperfect and fast, present it, and let the feedback do the work. Iteration is more reliable than specification.<\/strong><\/p>\n\n\n\n

7. Treat AI like a junior developer – useful, but you’re still responsible<\/h2>\n\n\n\n

Kristyna uses AI heavily in her work, but her framing of it is consistently grounded: “I treat it like a junior developer who I can ask questions. It can go off, think, come back to me with results. But I’m checking.”<\/em><\/p>\n\n\n\n

Kristyna is not alone in this thinking towards AI, and gave a good example to further prove the point. She once used Claude to build a Spark version upgrade smoke test notebook in about 30 seconds, and then build a second script to scan a repo and extract all relevant libraries and queries.<\/p>\n\n\n\n

So, a task that previously would have taken a full day, took under an hour – but she was directing every step of it, not just running whatever the model produced.<\/p>\n\n\n\n

Kristyna summarized: “I’m not worried about AI taking my job. I’m more worried about the people who think AI is going to take my job. I’m a little bit worried for them!”<\/em>

The key takeaway: the people who will benefit most <\/em>from AI are those who can clearly define a process, break it into steps, and evaluate whether the output is correct. That requires genuine technical knowledge which AI amplifies but does not<\/em> replace.<\/strong><\/p>\n\n\n\n

8. Build your skillset – then share what you know with your team<\/h2>\n\n\n\n

One of Kristyna’s most practical AI habits: she builds Claude skills for repeated tasks. Her Power BI optimization skill takes in tabular model scripting language (TMSL)<\/a> code, Measure Killer<\/a> output, and DAX Studio<\/a> results, and produces a structured analysis of everything that needs improving.<\/p>\n\n\n\n

Calculated columns<\/a> taking up excess space, for example, or duplicate measures, compressed tables…etc. Things that would take hours to audit manually.

And critically: her consulting team shares these skills<\/em> in a Microsoft OneDrive folder. What one person figures out, everyone benefits from.

Top tip: if you’re doing the same type of analysis or review regularly, it’s worth investing time to systematize it. Whether an AI skill, a template, or a documented process…share it! The compounding effect of shared knowledge inside a small team is significant.<\/strong><\/p>\n\n\n\n

9. Testing isn’t optional (but needs a ‘why’)<\/h2>\n\n\n\n

Kristyna is honest about the reality of testing in consulting: UAT (user acceptance testing)<\/a> is usually the bottleneck. Why? Put simply: it depends on business users who are already overloaded. And while automated testing can (and does) help, it’s only really beneficial for the failure modes you’ve already imagined.

Kristyna’s favored approach to testing combines Python-based automated tests with a source-of-truth validation mindset. Outputs are always<\/em> checked against something that the business can confirm is correct.

However, her most important piece of testing advice is is about documentation: “There should always be a ‘why’ associated with a test, because people will start ignoring failures if there’s no context.”<\/em>

The key takeaway: if you’re building automated tests, treat the failure message just as important as the test itself! A cryptic failure that nobody understands is almost as useless as no test at all. Write for the person who may one day have to debug this at 11pm. It might even be you<\/strong>.<\/p>\n\n\n\n

10. Community involvement is a career accelerant (and<\/em> it’s fun!)<\/h2>\n\n\n\n

Kristyna grew up watching her father, Steve Hughes, build his career through the SQL community via Data on Wheels<\/a>. Then, when she got into data herself, she already understood the power of community participation.

Fast forward to now and Kristyna speaks at conferences and runs a user group with quarterly happy hours specifically designed to attract younger professionals. And she credits much of her career growth to the people she’s met in the community.

“When you find a user group, your doors just open,”<\/em> Kristyna says in the episode. “You feel like you can finally ask somebody the questions you’ve been wondering about. It can feel very isolating if you’re the only person on your team who does X, Y, and Z.”<\/em>

It’s true that remote work has eroded some of that sense of belonging in tech communities (a point eluded to in the episode), which is why attending in-person events is more important than ever. They’re irreplaceable for the kind of serendipitous connection that actually moves careers forward. <\/p>\n\n\n\n

Whether it’s PASS Summit<\/a>, SQL Saturday<\/a>, PGDay<\/a>, or a smaller, more local user group – they’re the heart of the community, and a big part of why<\/em> people love their respective industry within data.

The key advice: if you’re not already involved in a user group, local meetup, or online community for your technology of choice, start there. Find the people who care about the same problems you do. The conversations you have there will shape your thinking in ways that courses and certifications simply won’t.<\/strong><\/p>\n\n\n\n

What do you think? Have any advice you’d like to share? Leave a comment down below!<\/strong><\/h4>\n\n\n\n
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Simple Talk is brought to you by Redgate Software<\/h3>\n
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FAQs: Breaking into the data industry – career top tips<\/h2>\n\n

1. Do I need a tech degree to get into data?<\/h3>\n
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No. Adjacent roles in admin, reporting, or operations are legitimate entry points. Kristyna got her start because she knew Excel \u2014 technical skills can be learned on the job.<\/p>\n<\/div>\n <\/div>\n

2. What's the fastest way to learn new tools?<\/h3>\n
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Learn them to solve a real problem. A concrete application makes skills stick far quicker than studying “just in case.”<\/p>\n<\/div>\n <\/div>\n

3. Will AI replace data professionals?<\/h3>\n
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Unlikely. AI is most valuable to people with genuine technical knowledge who can direct it and check its output \u2014 it amplifies skill rather than replacing it.<\/p>\n<\/div>\n <\/div>\n

4. How important is community for career growth?<\/h3>\n
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Very. User groups and conferences open doors that courses can’t. The conversations you have there shape your thinking in ways that self-directed study rarely does.<\/p>\n<\/div>\n<\/div>\n <\/div>\n

5. Is consulting worth considering if I feel stagnant?<\/h3>\n
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Yes. Consulting exposes you to more technology, industries, and problem types than most in-house roles ever will \u2014 variety forces learning in ways self-study rarely does.<\/p>\n<\/div>\n <\/div>\n

6. What's the best way to gather requirements for a dashboard?<\/h3>\n
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Build something fast and imperfect, then show it. People find it much easier to say what they don’t want once they can see something concrete. Get to feedback quickly.<\/p>\n<\/div>\n <\/div>\n

7. What should I do if I get rejected for a job or opportunity?<\/h3>\n
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Keep going! Kristyna’s first MVP nomination was declined. Consistency over time matters far more than any single attempt \u2014 the body of work accumulates.<\/p>\n<\/div>\n<\/div>\n <\/div>\n <\/section>\n\n\n\n


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