How SQL and Data Visualization Drive Success: Insights From Scott Davis

If you’re thinking about becoming a data analyst, one thing is clear: SQL is your golden ticket. Whether you’re crunching numbers, building dashboards, or connecting data tools like Tableau, SQL is everywhere. I’ve got a great interview with Scott Davis—a pro with years of experience—who shares exactly how SQL fits into his day-to-day workflow and what skills you need to make it in the data game.

The demand for data analysts is booming right now. According to a recent report from Indeed, data analyst jobs are expected to grow by about 20% between 2023 and 2030 – way faster than most other roles out there. It makes sense when you think about it; companies across all industries need people who can make sense of their data and turn it into smart decisions.

And here’s the kicker – the average salary for a data analyst in the U.S. is around $70,000 a year, depending on where you live and your experience. Not bad for a job that’s only becoming more important as businesses lean harder on data to stay competitive.

For those breaking into this field, understanding SQL and tools like Tableau is no longer optional; it's a must. But what does this look like in the real world? What does a data analyst actually do day-to-day and why do they need SQL?

Why do Data Analysts Need SQL?

To find out, we caught up with Scott Davis, a seasoned data analyst with over six years of experience building dashboards and extracting insights using SQL and Tableau. Scott's career includes stints at Meta and Flextronics, where he worked on high-impact projects that turned raw data into actionable business strategies.

In this interview, Scott shares the exact skills he uses daily, how SQL fits into his workflow, and advice for anyone looking to step into a data analyst role.

Please introduce yourself and explain your current role in data analysis.

Scott: I’m Scott Davis, a Bay Area-located data analyst with 6+ years building business dashboards using Tableau. I have had contract and consulting roles at companies like Meta and Flextronics. My contract at Meta recently ended and, since then, I have been presenting at Tableau User Groups, as well as adding to my project portfolio.

How long have you been using SQL in your role and what are the main tasks you accomplish with it?

Scott: SQL is something I use every single day and it’s part of every step of the dashboard development process, from start to finish. Whether it’s building pipelines, exploring data sources, or connecting tools like Tableau, SQL keeps everything running smoothly. It’s not just a tool: it’s the backbone that supports everything from building the initial data pipeline to delivering insights that drive decisions.

During pipeline development, I write SQL queries to create tables that form the foundation of any project. These tables act as the building blocks for clean, organized data, making it easier to analyze later.

At the data source investigation stage, I rely heavily on ad-hoc queries to dig deep into tables, check metrics, and validate that everything is accurate and reliable. This step is important—if the data isn’t clean, the dashboards won’t be either.

When it’s time to connect data to BI tools like Tableau, SQL allows me to write tailored queries that power the dashboards. These queries ensure that the data coming through is streamlined, accurate, and ready for visualization. For example, Tableau has a feature where we can use parameters (UI) selector) to dynamically change the query from the dashboard. Our team has used this to change the date range of the data when it is too large.

Even after development, SQL continues to be just as important during post-development support. I often use it to answer client questions, debug issues, check the quality of existing metrics, or explore potential features for the future. SQL helps bridge the gap between raw data and actionable insights at every step of the way.

Which topics have you managed to understand more deeply with LearnSQL.com? Which courses would you recommend for beginner data analysts?

Scott: LearnSQL has been a game-changer for me, especially when it comes to understanding how SQL can help create key metrics that are critical for any data analysis project. I’m talking about things like mapping hierarchies, running totals, moving averages, and calculating the percentage of a total. These metrics might sound simple, but they are incredibly powerful for delivering insights that businesses rely on.

What I appreciate about LearnSQL.com is how it provides a clear, structured framework for learning these concepts. A lot of other resources, other online SQL platforms or even YouTube tutorials, tend to scatter these topics. You get bits and pieces here and there, but not a standard approach to truly mastering SQL. LearnSQL.com fills that gap, making it easier to learn and apply SQL in a real-world setting. To give you a glimpse of how awesome LearnSQL.com is, here are some of my favorite articles: What Is a Running Total and How to Compute It in SQL?, Moving Average in SQL, and Revenue Growth in SQL.

For beginners, I always recommend starting with the basics. First, focus on SQL itself. Learning how to write clean, efficient queries and understanding how databases work is the foundation for any analyst. Once that’s in place, you can move into data visualization. Tools like Tableau are great for visualizing data in clear, actionable ways.

After that, I suggest practicing case questions. These are great, not just for preparing for interviews, but also for tackling real-world challenges. Finally, don’t overlook beginner statistics. You need a basic understanding of averages, variance, and confidence intervals to truly analyze data effectively.

Domains:

• Reporting/Data Viz
• Experimentation
• ETL/Cloud support
• Domain specific/other

Once you’ve mastered the fundamentals, you can start exploring specific domains. If you’re drawn to reporting and visualization, focus on BI tools like Tableau or Power BI, and work on designing clear, actionable dashboards. For those interested in experimentation, tools like Python or R will help you dive into statistical analysis and A/B testing. If you prefer working with data workflows, the ETL space—which involves cloud technologies like AWS or Azure and tools for managing large-scale data—is a great area to explore.

At the end of the day, mastering SQL and building a solid foundation will open doors.  LearnSQL.com simplifies the process of mastering SQL with a step-by-step approach that keeps you on track.

As data analysis evolves, what specific skills or areas within your job are you looking to enhance?

Scott: I believe each of the domains will be getting more specialized tools. I am more on the reporting/Data Viz side, so I am looking to embrace new tools to help me write better SQL queries and create better dashboards. For example, on the SQL side, I am always on the lookout for better documentation to optimize queries, as well as new tools like ChatGPT to define the problem more. Over the past 6 months, I have started using Ai tools to help write queries and I am still trying to refine the process to help code better.

In your industries, how do you see the role of SQL and data analysis evolving in the near future, and what skills should candidates focus on to stay competitive?

Scott: On the reporting/Data Viz side, there are ‘codeless’ tools being developed to help ingest data into BI tools, such as Tableau Prep and Alteryx. While those tools may not replace SQL in 100% of circumstances, they still rely on data management fundamentals and can become another tool in the workflow.

Another consideration is your team’s knowledge and best practices. Depending on that, you might want to go with the ‘codeless’ route or stick to the traditional SQL route. In my last team, we always used the traditional route. We wanted not only to rely on the tools but also to fully understand how everything happens under the hood.

What advice would you give to those looking to pursue a career as a Data Analyst?

Scott: Focus on mastering the SQL fundamentals first because, without a strong base, everything else will feel shaky. Learn how to write clean and efficient queries, and understand how data flows. From there, explore the different areas of data analysis that interest you most. Try to tackle real-world challenges effectively and keep growing as a data analyst.

If you want to check out more of Scott's work,  take a look at his Tableau Public profile or his blog, or connect with him on LinkedIn here.

How to Get Into Data Analysis

Scott’s story shows us that becoming a data analyst starts with SQL. You can’t escape it—and that’s a good thing. It powers everything from pulling clean data to building metrics and dashboards that businesses rely on every day.

The question is, where do you start? I’ll save you the search: SQL for Data Analysis is exactly what you need. This course doesn’t just throw SQL commands at you—it shows you how data actually works in real-life scenarios.

You’ll see how to organize messy datasets, build queries that join and filter data, and create those all-important metrics like running totals, averages, and percentages. It’s the kind of hands-on practice you’d get on the job—but in a way that’s easy to follow.

So, if you’re serious about learning SQL and want a clear path to follow, check out the SQL for Data Analysis Track. It’s perfect whether you’re brand new to SQL or just want to sharpen your skills. Go give it a look—you’ll thank yourself later.