Plata Names Ivan Shchukin Data Warehouse Head
Plata’s head of data warehouse, Ivan Shchukin, oversees a system that processes the company’s financial information with a one-hour delay, a speed that stands in stark contrast to the standard 24-hour industry lag. Shchukin joined the fintech firm three and a half years ago when the company had no centralized data warehouse and a small team operating out of what he described as a “regular garage band” setup. Under his leadership, the department has grown to more than 50 people, organized into roughly ten specialist groups. This expansion has allowed the company to automate roughly 80 to 90% of its data processes, with built-in quality checks that help the team react to transaction failures or metric drops within hours.
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The rapid growth of the team coincides with a broader expansion of the business. Plata recently obtained a banking licence and plans to enter additional Latin American markets, requiring Shchukin’s department to scale its operations accordingly. He is currently developing automation and tooling designed to make each new market deployment faster and leaner than the previous one. To support this, he is piloting AI agents to handle routine internal support queries, with a target of resolving 80% of basic questions automatically. He is also overseeing a shift away from conventional business intelligence software, planning for analysts to generate their own data visualizations using AI tools within the next year. Shchukin emphasizes that this transition requires rigorous data documentation before any AI layer can function reliably.
Shchukin made two foundational decisions early in his tenure that have defined the department’s direction. The first was to collect all company data with full version history, logging every change to every record. The second was to process that data with a maximum one-hour lag. He was an early advocate for analytics engineering as a hiring discipline, building his team around specialists who sit between raw data infrastructure and business analysis. This focus on usability has given the company clearer processes and a more coherent path for scaling. Currently, about 60% of staff use AI tools daily. Shchukin sets a high bar for the team, stating that “AI is like a calculator in 1980 – you just need to use it to stay relevant.”
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Historically, companies often built their data teams around data engineers who focused on the technical infrastructure, leaving business analysts to struggle with raw data. Shchukin’s approach flipped this dynamic by prioritizing analytics engineers who could translate complex infrastructure into usable business insights. This shift mirrors a broader trend in the industry where the demand for data accessibility is outpacing the supply of traditional engineering talent. By positioning his team to handle the bridge between technical requirements and business goals, Shchukin has created a structure that is resilient enough to handle both the current operations of the Mexican market and the potential volume of future international expansion.
