Cracking the Code for Digital Insights: Meet Marco Cantuaria

Can you start by telling me about your professional history leading up to your current role at Data Hub and where your drive for data came from? 

I initially aspired to become a doctor, heavily influenced by my mom. When I was 18, I didn’t have a clear vision of what I wanted to pursue. Eventually, I stepped into engineering and graduated in petroleum engineering. 

I had the opportunity to work for two operator companies before Data Hub. At the first, I worked in the R&D department, which required analytical skill, patience and effective communication.  At the second company, I worked in workover/drilling, which involved lifting heavy tools, thinking quickly and having courage. It was like enjoying both heavy metal and classical music: two contrasting worlds.
 

Through these experiences, I discovered my true calling lay somewhere in the middle, which I found to be programming. I’ve had an interest in programming since my teenage years, when I tried writing my own game. I even wrote my undergraduate thesis using machine learning and production data from the North Sea’s Volve Field. I began integrating programming into my daily work routine, starting with C#, then Python. 

Realizing that I no longer desired a traditional petroleum engineering career, I took a leap of faith in 2021 and pursued a Master of Science in computer science at Rio de Janeiro State University. Around the same time, I got my first position as a junior data scientist in a startup company in Sao Paulo, Brazil. I worked there for about eight months before joining a clothing brand in Rio de Janeiro as a data scientist. 

Less than a year later, I found a LinkedIn job careers listing from CGG, and not even knowing it was an international position, I sent my CV. When I found out the job was in the UK and that Data Hub was interested in me, I couldn’t be happier.
 

What has the transition from petroleum engineering to data transformation revealed?

The transition has shifted my mindset from merely generating data to asking important questions: What data are we gathering, and more importantly, why are we generating it? It’s no longer solely about collecting data in the traditional manner, but rather ensuring it’s collected in a way that permits reusability, compilation and analysis. 

The energy industry, known for its multidisciplinary nature, is now incorporating domains such as statistics, software engineering, data engineering and data science. For the first time, we’re experiencing the convergence of historically separate domains like geochemistry and PVT being evaluated together, leading to valuable insights. In my opinion, we’re experiencing the most crucial period in the energy industry since the offshore movement of the 1940s.
 

How challenging was the career transition for you, and what advice do you have for anyone considering a similar path?

The tech industry is renowned for its rapid innovation and transitioning into it can be challenging. Skilled professionals are in high demand, but success requires self-drive and the ability to continuously adapt and learn. The constant need to learn new programming languages, frameworks, libraries, tools and software can be daunting, but it’s worth it. I’ve rarely come across a programmer who doesn’t love what they do. I certainly do. 

My advice to anyone considering the move is simple: Learn to love what you do. There is nothing more satisfying than writing high-quality, reusable code that helps others with their tasks. Universities are excellent places to learn, but coding bootcamps, books, websites and learning platforms are also valuable. Many of these resources connect students with top universities and provide certifications and free courses. Building a portfolio, showcasing your skills and posting on platforms like GitHub go a long way in establishing yourself.
 

How do you think drilling and workover can benefit from the modernization of the industry?

Drilling is one of the costliest activities in the energy industry. Although the processes are well-established and the documents are often templated, it doesn’t mean they are easily digitalized. Often, they are created solely for reference purposes, not to be compiled and compared, which makes transformation difficult. 

Applying analytics, particularly within a data science context, uncovers numerous areas for improving processes and identifying key issues to address. To draw a parallel, consider how baseball implemented analytics and faced significant paradigm shifts—as well as resistance from traditionalists. Similarly, the drilling and workover domain, with its conservative nature driven by risk management and high stakes, faces inertia when it comes to embracing change.
 

Can you walk me through a day in the life as a data engineer and some of the most exciting projects you have been a part of in Data Hub?

In a nutshell—wake up, drink a lot of coffee, face your doubts and go to work. 

My daily tasks depend on the project I’m working on. Currently, we’re working with an operator to transform various file formats like PDFs, Excel spreadsheets and CSVs into a more valuable and usable digital format. This involves tackling duplicates, inconsistent formatting, naming issues and more. The first step is organizing ideas and creating an action plan with well-defined tasks. The best programmers are those who think and plan before they code. 

Once the problem is understood and a potential solution is identified, it’s time to write code. For me, there’s nothing more satisfying than an organized, clean script. Data engineers must follow coding conventions specific to the programming languages they use. When we say Python, that means PEP 8, which means virtual environments, version control and Docker (when possible). Most likely, you will end up writing a couple of SQL queries and using GraphDB as well as a little bit of Excel. 

We’re frequently tasked with finding solutions to seemingly unsolvable problems. In such cases, collaboration among subject-matter experts and programmers is essential to not only understand the requirements but determine the best approach to tackle the issues. 

I like to emphasize that some of the most complex coding problems I’ve encountered were solved when I wasn’t actively coding. Often, breakthroughs happen over lunch, during coffee breaks or while watching a movie.
 

Where would you like to see yourself in five years?

In five years, I envision myself in a technical lead position. Programming is my passion, and I want to continue pursuing it while becoming a mentor and point of reference for my co-workers. Personally, I hope to remain with CGG. It has become a place where I feel a sense of belonging, where I can be myself with people I enjoy working with and who value my contributions. 

In my free time, I’m also an indie game developer and musician. I love competing, so in five years I hope to be a data engineer from 9 to 5 and a rockstar from 5 to 12, so I can still get enough sleep!