In college I was actually a CompSci major. It was a ton of fun coding and getting to solve cool problem sets / projects, but some of the classes were unbelievably difficult, and the IDEs were also frustrating to use. One of those courses was CompSci 316 - Introduction to Databases. Everyone hated this class, people dreaded the problem sets, the exams, and more. At the time, SQL really made no sense to me and I couldn't conceptualize why this was important when I could navigate datasets in Python with ease. JOINs were my arch-nemesis, and I couldn't understand why anyone would voluntarily work with databases. Fast forward to today, and I'm (somewhat actively) seeking out opportunities to query data myself.
In finance and growth equity, data was also key - every number was meticulously checked, every decision came from a model which was projected based on a number of underlying assumptions, all numerical and reasoned out.
Even when I joined my current FT role, sub 10 people, data was always the basis of every decision. Making informed product decisions, validating customer pain points, and understanding user behavior patterns aren't just buzzwords—they're the foundation of my day-to-day responsibilities. A large percentage of how I interact cross-functionally involves data querying so that I can help inform product roadmap / direction, validate customer problems, explain shortcomings / improvements to our platform, propose experiments, and more.
In today's hyper-competitive startup landscape, data isn't just a byproduct of our operations—it's the compass guiding our entire product development process. Having direct access to this data through SQL allows growth professionals like me to:
"The great thing about fact-based decisions is that they overrule the hierarchy." Jeff Bezos
Learning SQL definitely isn't exactly a walk in the park, especially for those of us without technical backgrounds. The syntax can feel clunky and unintuitive. Concepts like normalization, primary keys, and query optimization can quickly overwhelm beginners.
But here's the exciting part—there's never been a better time for non-technical professionals to learn SQL. Tools like Cursor and LLMs have given non-technical users the ability to write code with the assistance of AI with more precision than ever before.
I've used Cursor for some amazing things in my normal day-job, from building CSV cleaners, to scraping websites, to interacting with APIs, to using BrowserBase, and tons more.
While I don't feel any of the free tools out there are built for the future / empower me to be more technical, they can allow you to take the initial step innto the SQL pool. Almost all of these are pretty tough on the eyes. Long term, we hope to democratize data access thru use of AI and a intuitive and pretty UI / UX.
pgAdmin serves as an open-source administration and development platform primarily for PostgreSQL. While it's incredibly powerful, the interface is overwhelming as a beginner. However a lot of people on my team use it due to its robust functionality.
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Redash quickly became one of my favorite tools. It connects to various data sources, allows for query writing, and—most importantly for me—creates interactive dashboards that can be shared across the organization. Because it's free, we use this for everything. It's pretty brutal in terms of UI / UX, organization, and sharing / collaboration. Also AI is nowhere to be found...
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Equals offers a fascinating hybrid approach—combining the familiarity of spreadsheets with the power of SQL. For someone coming from an Excel background, this was a revelation. I'm a big fan of the Equals approach as well, and am friends with a handful of people on their team :)
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One unexpected benefit of learning SQL has been improved communication with our engineering and data teams. Speaking their language—even at a basic level—has transformed our interactions from "can you pull this data for me?" to collaborative problem-solving sessions.
Effective database query tools facilitate this collaboration through:
Shared query libraries where common analyses can be stored and accessed by anyone. We're doing this in a janky way in Redash but aim to do this more seamlessly in Galaxy's Collections feature.
We still think there's a ton of features missing from a collaborative, AI-enabled query tool, which is why we're building many of these into Galaxy ourselves :)
This collaborative approach ensures everyone operates from the same data foundation, eliminating the "multiple versions of truth" problem that plagues many organizations.
While the current landscape of SQL tools is impressive, the most exciting developments lie at the intersection of database querying and artificial intelligence. These innovations promise to make SQL even more accessible to non-technical users.
As someone who once considered SQL my academic nemesis, I'm now surprisingly passionate about creating more accessible database tooling. The next generation of query tools needs to:
The ultimate goal isn't just making SQL easier—it's transforming how entire organizations relate to their data. When everyone from marketing specialists to product managers can independently answer data questions, the pace of innovation accelerates dramatically.
The most surprising outcome has been how empowering it feels to answer my own data questions without depending on others. That autonomy has made me more effective in my role and has changed how I approach growth challenges.
The ability to directly query databases has fundamentally transformed my effectiveness in growth. What once required a formal request to the data team can now be accomplished independently in minutes, allowing for faster iteration and more data-informed decisions.
For other non-technical professionals hesitating to dive into SQL, I offer this encouragement: the learning curve is real, but it's nowhere near as steep as it once was. Modern tools have democratized database access, and the skills you'll develop will pay dividends throughout your career.
As we look toward the future of database querying, the integration of AI capabilities promises to make these tools even more accessible while maintaining their analytical power. The combination of human curiosity with technological advancement creates a future where truly data-driven decisions are within everyone's reach—technical background or not.
What's your experience with SQL as a non-technical professional? Have you found other tools that make database querying more accessible? I'd love to hear your thoughts in the comments below!
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