The Modern Data Stack Expert Behind ~$3 Billion in Funding
Marcelo Soto’s life is defined by a love of data, and a passion for discovering efficiency in inefficient systems.
At nine years of age, the young Mr. Soto received his first computer—an IBM XT, the second in the line of IBM personal computers—and promptly decided to learn programming to create his own AI using if/then statements. Although he naturally failed at this laudable endeavor, the incident illustrates Mr. Soto’s lifelong calling for data and its usage.
“I realized the rabbit hole was much larger than I’d anticipated, so I started learning as much as I could about the general subject of IT, Data and then AI,” says Mr. Soto.
After completing his studies in IT at the Universidad Tecnológica Nacional in Buenos Aires, Mr. Soto recognized that the best way to learn about tech was to start working on real-world business projects instead of putting too much time in theory.
In 2011, Mr. Soto’s love for data grew stronger than ever while overseeing mission critical Oracle databases at Toyota Argentina. The experience also introduced Mr. Soto to the Japanese philosophy of kaizen—a core principle in lean manufacturing that focuses on small improvements at a time which ultimately result in significant changes—and the just-in-time methodology, which drives efficiency.
“I realized that, no matter what technology appears in the future, data will always be at its foundation,” says Mr. Soto.
Mr. Soto decided to focus the rest of his career on the modern data stack and how to get value from it.
Data solutions that helped fund $3 billion
Before becoming an in-demand data platform consultant in 2021, Mr. Soto served as an integral member of several companies—including startup and hyper-growth companies—that exposed him to the many facets data problems can have.
“In one company, they had a tech problem with their data. In another, they had great tech, but real-world issues sat behind their data problems. Or they could have both solved, but had huge semantic misalignments. Each experience provided unique challenges that required highly tailored strategies, and I learned key lessons in each one of them,” he says.
After rolling his sleeves up to manage data for the telecommunications giant Claro—where Mr. Soto managed and optimized Oracle, MS SQL, DB2, and MySQL databases—he decided to embrace a then-controversial technology: Data in the Cloud.
Cloud-tech early-adopter
“Back then, moving to cloud tech was risky. Many naysayers existed, and not everyone was convinced it would be the way of the future,” Mr. Soto says.
Despite the market uncertainty, Mr. Soto’s belief in the new technology led him to Argentina’s second-largest travel company Almundo, which had embraced cloud technology and was moving its operations to AWS—then a fledgling cloud service.
The experience at Almundo convinced Mr. Soto that there was no stopping cloud technology. Before the cloud, companies would take months to set up new servers and data stacks, whereas the same could be achieved in a few minutes in the cloud, with unprecedented levels of flexibility and automatization.
Still, cloud tech was in its infancy, and numerous errors and challenges existed that Mr. Soto and his team had to overcome. In some cases, even the official APIs didn’t work.
Mr. Soto navigated those complexities successfully as Senior DBA of multiple MySQL, MariaDB, MongoDB, Cassandra, PostgreSQL, ElasticSearch, Redis, and memcache data stores, spanning a platform of approximately 400 instances and clusters, all of them set up as high availability, configured and orchestrated by automatization tools.
Rappi—a hyper-growth startup, and Mr. Soto’s $2-billion data room
In 2018, Rappi—LATAM’s equivalent of Instacart and Doordash—hired Mr. Soto as its Head of Data Platform to help the company deal with the typical problems that startups begin experiencing when they hit hyper-growth mode.
At the time, the company had several hundred production databases and way too many administrators, but no data team in place to fix the daily errors everybody was bumping into.
Mr. Soto transformed the chaos into order by finding the right people, tech, and procedures, and implementing a proper, scalable, modern data stack.
He also created a world-class data room solution that provided VCs such clarity of understanding into Rappi’s underlying data that they collectively dropped $2 billion into the startup.
Softbank Vision Fund (SVF)—the world’s largest tech investment fund—invested $1 billion.
Providing data solutions for another $1-billion
Mr. Soto continued to seek out new and different challenges, driven by his love of data and his personal belief in efficiency.
This drive led him to work at multiple companies, which have collectively raised nearly $1 billion in funding.
In one of those companies, Mr. Soto ran into a common problem that isn’t always apparent—a company with great tech and people, but whose business processes interpret data differently from each other, leading to conflicting opinions about almost all of the company’s indicators.
“Data can’t make magic. If the data from the real-world business is wrong or interpreted incorrectly, the resulting insights at the end will also be wrong,” he says.
The experience forced him to think “outside the box” to find solutions for these semantic problems. The company had the tech to solve the issue but lacked agreement on how to think, work and interpret their processes and the resulting data. Mr. Soto solved this through an educational and awareness campaign that brought the entire company into agreement with their data so they could finally use it to make proper decisions.
All told, Mr. Soto worked across multiple sectors, driving data solutions to improve companies that have received funding from (among others):
- Y Combinator
- Softbank
- DST Global
- Third Point
- Octahedron
- Andreessen Horowitz
- Sequoia Capital
- SoftBank Group
- Homebrew
- TriplePoint Capital
- Inter-American Development Bank
- Bedrock Capital
As demand for Mr. Soto’s expertise grew, he realized he could better serve companies as an independent data consultant. This role quickly grew into companies like BlueYeti, where Marcelo is dedicated as CTO to helping companies align their business value with their technology and realize their full potential with the modern data stack. .
Now, with the emergence of AI, Mr. Soto sees an opportunity for companies to leverage the modern data stack to bring data and knowledge to every employee, thus extending the capacity of what each individual human can do—if the underlying data is handled right.
“Providing the right data to AI is of paramount importance if you want businesses to obtain real value from this new technology,” says Mr. Soto.
BlueYeti—The modern data stack company helping companies define AI business outcomes
Mr. Soto recognized that the underlying data serving modern AI solutions is usually cluttered, messy, and disorganized. He and his co-founders decided to create a Modern Data Stack and AI Center of Excellence called BlueYeti to help companies define business outcomes for their AI solutions and make them happen efficiently.
Mr. Soto explains that many of the companies trying to implement AI are doing so without clearly defined business outcomes—and also without sound data. Any company doing that will run into trouble, he says, and BlueYeti’s purpose is to help those companies build the necessary foundations that allow AI to reach its full potential in their hands.
Mr. Soto’s passion for data and tech continues to fuel his drive for efficiency and business value. His experience in complex situations taught him that no silver bullet exists for anyone’s data problems. Each company has unique needs, and each must therefore be approached uniquely.
This attitude permeates all of BlueYeti, recognizing that no single solution exists to drive business outcomes for a company using AI, but that any solution must rely solidly on good data. Mr. Soto’s experience, and the team he’s put together at BlueYeti, intend to deliver that good data platform so that AI can be used effectively, and ultimately drive business value.
“We even provide our own AI solutions that work directly inside the Modern Data Stack. That way, our AI solutions live right next to the data it needs to feed its capacities, allowing quick delivery of Agentic AI capable of crunching on big data, and acting accordingly on real world processes,” Mr. Soto says.
