From Maps to Models: My journey advancing Geospatial Science to Data Engineering & Science and how you can get Started Too.

7 min read2 days ago

I have a confession;

For over 24 months, I’ve hesitated to call myself a “data engineer.” Instead, I think of myself as a “big data processing guy” — someone who moves fluidly across methods, architecture, and a myriad of technologies to meet the unique demands of my work. And I love it that way.

However, as I elevate my ambitions, I’m excited to share the insights I’ve gained on this journey and bring you along for the ride.

As challenging as it already feels, over the next six weeks, I’ll be rolling out a blog series covering six different portfolio projects, each offering practical and accessible ways to get your foot in the door of data engineering & science. I’m here to show you how to build on your current skills — especially if you’re coming from a geospatial or statistical background — without needing to overhaul your career progress and start from scratch.

So, let’s jump in. Who’s this series for?

Data Processing Enthusiasts, especially those with experience in handling geospatial data in both open-source and restricted environments. I’ll share examples of scalable, practical methods that can be adapted or modified across different settings with minimal effort.
Aspiring [geo]Data and ML Engineers: While, I cannot promise every detail in just six project, I aim at tackling the niched and simple approaches that extend your chance to learn foundational concepts.
Self-Taught Learners: If you feel stuck in an endless loop of re-learning the basics or find that the media environment merely exposes you to tools without depth, these projects will provide you with a sense of direction and motivation to break into more advanced topics. Think of me as your virtual mentor.
Folks with Limited Data Access: Throughout this series, whenever possible, I’ll offer insights into geospatial concepts that you can apply even with limited data, because I know that’s the reality for many.

My Journey: How Did I Get Here?

Alright, here’s the personal part. It has been quite a journey for me to bring clarity and order to my growth process, which is partly what motivates me to share my refreshingly messy journey and even gain better clarity of my vision. I call these experiences ‘happy mishaps’ — a mix of passion, curiosity, and, honestly, a lot of trial and error. I hope it resonates with you in the same way. It all started with my love for maps and data, but my real goal has always been to understand how data moves — from collection to processing, to storage, to visualization. And sure, most people only ever see the end of it: a dashboard, a pretty map, or a CSV file. But for me, it’s about understanding the entire data spectrum.

If you’ve worked with geospatial data, you know it can feel like a bit of a closed world. I’ve come to see that many working in this space function as software engineers, creating a natural gap between GIS tool users and the developers behind the algorithms and frameworks. On one side, there are GIS professionals focused on spatial analysis — selecting projections, running geoprocessing tools, and generating results. On the other, there are those designing the infrastructure that powers these operations. In a data-driven world where (geo)data insights are crucial, I believe scientists in this field should cultivate custom tools for tailored solutions. Working with geospatial data often involves strict checks and balances, attribute transformations, and high-security requirements. I’m here to dig into these topics with you and connect the dots between end-users and the architectural side of data science.

Reality Check: My World of Data Engineering and Science

Now, let’s talk about data engineering — what it really is and what it isn’t. If you’ve spent time on LinkedIn, X, or other networks, you’ve probably seen “data engineering” pop up so often that it feels saturated yet barely captures the complexity of working with location-based data. I’ve pondered this myself, and my reflections are exactly why I’m writing this.

For me,

data engineering is a field that constantly adapts, shaped by the four big data V’s, the work environment, and specific project needs. The term “big data” ultimately guides key choices — what tools to use, how to design the architecture, and most importantly, where to source the data. This is what makes the work dynamic and rewarding!

As someone who has primarily worked with geospatial data, I can say that data engineering and science are integral to our experience, even though we often approach them through commercial and open-source tools like ArcGIS, QGIS, or web GIS — tools that can feel somewhat isolated from the broader data engineering community. The fundamentals of geospatial data management, processing, and security are closely aligned with core data engineering concepts, even when the terminology differs. Advancing to this level of learning will be straightforward for you too. You’ll encounter new terms like “batch processing” and “stream processing” that may not have been part of traditional geospatial training but are now essential as data scales beyond what we could have imagined. This series will guide you in adapting these concepts to a geospatial or any other data context.

Why follow? : What You’ll Get from This Series

More than just tech, a guide for Long-Term Growth: Consider this blog series as your virtual mentorship. I’ll guide you to the specific keywords you need for your next YouTube practice video, book chapter, or even a boot camp, if you choose that as a learning pathway. My goal is to help you expand your expertise without needing a complete career overhaul. This has been my journey, and I’m confident it will be helpful for you, too.
Hands-On Practice and lessons in resilience: I’ll be creating these projects in real-time, from scratch, as part of building my own portfolio, giving you a true hands-on experience. You’ll be able to practice alongside me, learning from both my successes and my mistakes. I won’t sugarcoat anything — this is as much a learning project for me as it is for you. I’ll be upfront about what works, what doesn’t, and why. This is a shared journey, for you and I to grow together. — You’re welcome to fork my portfolio to create yourself one too.
Staying Grounded Amid the Multitude of Tools: This series will help you stay grounded among the vast number of tools and subscriptions available, showing you how to tailor each to create meaningful, executable projects. My aim is to keep you focused on launching, executing, and completing projects with any tools or tech stacks, beyond just keeping up with updates on tools.

The Bigger Picture: Building Skills for Today’s Data Challenges

By the end of this series, my goal is for you to feel confident navigating the overarching concepts of data engineering and understanding how they intersect with geospatial data wherever possible. You’ll gain insight into the tech glossary, learning how terms evolve and take on different meanings in various contexts. Together, we’ll even create a glossary and revision guide by the end — something you can revisit and expand over time.

I also want you to feel prepared, no matter your resources. Whether you’re working with the latest tech stack or on a tight budget, I hope you’ll leave with tools to create quality, and impactful results. With each project, I’ll be building not only my portfolio but also your confidence to engage with this integrated spectrum of real-world data challenges.

Let’s Connect and Keep Growing

I’d love to hear from you as we build these projects together. You can explore my portfolio, subscribe or follow this blog, and reach out if you’d like to discuss any specific challenges. This journey is all about learning in real time, and I’m excited to share it with you. If you’re ready to navigate the world of notifications, constant new tech, and a flood of data, let’s dive in — staying grounded while moving forward together.

I hope you’ll find this blog series both inspiring and practical — a roadmap to help you move from “just maps” to a comprehensive understanding of data engineering. Here’s to building a bridge from geospatial science to data engineering, one project at a time! A special shoutout to Prof. Maxwell, whose coursework list ignited my passion at this depth: WV View Courses.