The energy sector is in the middle of an infrastructure overhaul: in 2025, investment in the global energy transition reached a record $2.3 trillion. As companies race to deploy cloud platforms and AI-driven analytics, experts such as Andrei Dzeikalo, who merge physical-world data with large-scale system design, are leading the transformation.
Andrei Dzeikalo, who transitioned into the energy sector from a background in geospatial information systems (GIS), is the poster child for a new breed of cross-disciplinary experts. By fusing spatial intelligence with large-scale system design, he’s reshaping how energy operations are monitored, optimized, and managed in real time.
Over the past decade, Dzeikalo went from processing map data at a regional GIS company to designing cloud infrastructure platforms and large IoT solutions for global energy operations.
What GIS actually demands
“GIS is essentially large-scale data engineering with unusually strict requirements,” Dzeikalo said. “The systems behind a navigation map process terabytes of spatial data from dozens of sources, such as government agencies, satellite providers, and field surveyors, each with its own format, update schedule, and quality standards.“
The data has to be cleaned, reconciled, validated, and shipped on tight release cycles. If a new highway opened last month and your map still shows a field, that’s a failure with real consequences.
“People assume maps are static,” Dzeikalo said. “But the infrastructure behind them is some of the most complex data processing I’ve worked on. You’re dealing with massive volumes, constant updates, and no tolerance for stale information – across dozens of countries simultaneously.”
High-volume data processing systems, automated quality assurance, geographic distribution, and strict freshness requirements are exactly what energy companies now need from their platform teams.
The hiring gap nobody planned for
The scale of demand is hard to overstate. McKinsey estimates that digital transformation could unlock billions in value across upstream, midstream, and downstream energy operations. Gartner projects that by 2026, 80 percent of large software organizations will have dedicated platform engineering teams. But the talent pool hasn’t kept up.
“The typical energy company knows its core operations inside out, including drilling, refining, and grid management, but has limited experience building the kind of cloud-native infrastructure that modern digital products require,” Dzeikalo said.
Engineers from Big Tech may struggle with the constraints of industrial sensor readings that arrive out of order, equipment spread across remote locations, and situations where bad data doesn’t just degrade the user experience but creates a safety risk
According to Dzeikalo, GIS engineers don’t have that adjustment period. “They’ve already spent years working with data mapped to physical reality, in systems where geographic distribution isn’t an abstraction, but a daily operational constraint,” Dzeikalo said.
An accidental career in maps
Dzeikalo studied information security, including system protection, network infrastructure, and cryptography.
“That training still shows up in my work,” he said. “I am unusually focused on access control, secure data flows, and system resilience.”
He stumbled into geospatial work when he joined a company building data for international navigation platforms. He started as a data analyst, but within a year he was writing software to automate the processing pipelines.
“The work kept getting more complex,” Dzeikalo said. “I built a system that tracked data freshness, measuring how quickly real-world changes like new roads, closures, or updated addresses actually showed up in the published maps.”
He also designed another system that automated spatial data generalization, a process that had previously eaten tens of hours of manual work per release cycle and compressed it down to a few hours of machine processing.
Both systems ended up being adopted across multiple regions. They became the internal standard for how the company measured coverage, freshness, and operational throughput.
“That was the shift for me,” Dzeikalo said. “I stopped thinking about data as something you process and started thinking about it as something people use to make decisions. That changes everything about how you design systems.”
Same problems, different maps
When Dzeikalo moved into the energy sector, the underlying architecture problems were remarkably familiar.
In GIS, one of the hardest challenges is data freshness: making sure your digital representation of the world keeps pace with the physical one. In industrial IoT, it’s the same problem, except instead of tracking whether a new interchange appears on a map, you’re tracking whether a pressure reading from a wellhead is current, accurate, and correctly routed to the right dashboard or predictive model.
Dzeikalo’s work in energy focused on building cloud platforms for industrial use: systems that let engineers and scientists pull live data from physical equipment, run analyses remotely, and leverage the full power of the designed-in digital twins, virtual replicas of real-world assets used for monitoring, testing, and optimization.
The technical challenges, such as ingestion, validation, transformation, distribution, were the same ones he’d been solving in GIS for years, just applied to different data.
What platform engineering actually looks like
Today, Dzeikalo designs cloud infrastructure for a platform that supports internal business operations across multiple regions, wiring dozens of different technologies together so product teams can move faster, without getting bogged down in infrastructure.
“A developer’s job is to build the product,” Dzeikalo said. “My job is to make sure the platform handles everything else, including rollout, scaling, security, and reliability. If they’re spending time on that, it means something in the platform is broken.”
The energy industry’s digital future depends on these kinds of engineers who have spent years building systems where the data has weight, location, and consequences.
“In GIS, I built systems that tracked how the world was changing,” Dzeikalo said. “Now I build systems that help people respond to those changes. Different tools, same challenge.”
