Plata Expands with New Data Warehouse System

When Ivan Shchukin joined Plata three and a half years ago, the Mexican fintech had no data warehouse. Today, it has a department of more than 50 people, near-real-time data processing and an ambition to render traditional business intelligence software obsolete.

The journey from that starting point to where the company stands now says much about how seriously Plata takes data – and how central that decision has been to its growth. Plata, which recently obtained a banking licence in Mexico, offers a suite of financial products including credit cards, debit cards and cashback schemes.

Ivan came to fintech by an unconventional route, grounding his origins as an electrical engineer, he accumulated experience across project management, software development and data analysis before being headhunted by Plata’s Chief Technology Officer.

Ivan Shchukin, Head of Data Warehouse at Plata, recalls the moment plainly. “Andrey Shelekhin, our CTO, said: ‘Ivan, you have an amazing skill set for our head of data warehouse role. Come to Plata – we need to create the best product.’”

Ivan accepted, and set about building something that he believes remains relatively rare in the industry. From the outset, he established two governing principles for the data warehouse that would shape everything that followed.

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The first was data collection. In place of storing only selected datasets, the team would capture all company data with full version history – meaning every change to a record is logged and preserved. The second principle was speed. Where many data warehouses operate on a lag, refreshing their data once a day, Plata’s system would update within one hour.

Ivan explains the thinking: “We want to collect all data in the company with full versioning. Everyone wants this, but most players in the market are afraid of it. I had no limitations and no fears.”

That one-hour latency target was not merely a technical preference. It was a business decision. In retail banking and fintech, the ability to identify and respond to problems, such as a spike in failed transactions or a dip in customer engagement, within hours rather than days creates a measurable competitive advantage.

Ivan says: “We can measure our business very precisely, like checking a person’s temperature. It’s the main reason we’ve been so successful in Mexico – we know what is working and what isn’t, and we can react in hours, not days.”

The warehouse now underpins approximately 80-90% of the company’s data processes, all running with automated quality checks and alerts. That level of automation has allowed Plata to keep its headquarters lean, even as the business has grown substantially.

The data warehouse department has grown from a team of 8 when Ivan arrived to more than 50 people organised across roughly 10 specialist teams. Managing that expansion has been one of the more demanding aspects of the role.

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Alongside managing growth, the team made an early structural decision that Ivan regards as significant: hiring analysts into a discipline called analytics engineering rather than the more established field of data engineering.

Central to Plata’s infrastructure is its relationship with Snowflake, the cloud-based data storage and analytics platform used by thousands of companies worldwide.

Ivan notes: “Snowflake removed a lot of pain for us. We can scale to a new country or a new domain in minutes, not months. That’s incredible.”

Looking at the 12 months ahead, Ivan has three clear priorities: geographic expansion of the data warehouse into at least one new country, the launch of the internal IDE and a significant increase in the proportion of staff using AI tools on a daily basis, similar to the trend seen in European fintechs.

He concludes: “AI is like a calculator in 1980. You just need to use it to be relevant – it’s not optional any more.”