With 15 years in the field and more than 30 completed projects, Igor Omelianchuk works with organizations that face fragmented data, ownership disputes, inconsistent reporting, or compliance friction. He maps what still works, then rebuilds around it: data flows, pipeline architecture, governance layers. His team integrates into existing Git and CI/CD workflows rather than working around them, which keeps transformation from stalling delivery. The priorities of that data modernization consultant are consistent: cut maintenance overhead, restore interoperability between fragmented systems, and rebuild only what is actively blocking growth.

Andrew Lychuk has spent 18 years on the architectural side of data modernization: delivery models, integration layers, and infrastructure decisions that determine whether a platform remains stable after the transition. He is brought in when the scope extends beyond data into the wider technical fabric of an organization. His job in data platform modernization consulting is to map what needs to change, what should not move, and how the data flow evolves without creating new fault lines. Multi-cloud environments and complex integration architectures are his regular terrain, with a steady emphasis on outcomes that hold under operational pressure.

Christopher Etheridge's work sits at a specific intersection: data platforms that must perform at scale inside strict regulatory boundaries. His 20 years include substantial work in government-adjacent environments where FedRAMP, CMMC, and ITAR are baseline requirements. He uses AWS GovCloud and Databricks as primary platforms, designing architectures where pipeline compliance is structural. His fixes register in measurable terms: processing time, audit readiness, and system reliability under load.

Sebastian Frassia works on the operational side of cloud and data platform modernization - the part that determines whether a rebuilt system holds under day-to-day conditions. 10+ years in cloud-native and distributed systems anchors the engineering side, but his focus sits on the governance and alignment problems that cause modernization to unravel after delivery: unclear data ownership, inconsistent pipeline behavior, cost overruns appearing months later. He designs with those failure modes in mind from the start, not after they surface.

Over 16 years, Scot Carlson has worked on transformation programs at organizations where the problem was not a lack of data but an inability to use it coherently. His client list - Deloitte, Samsung, WPP, Publicis - reflects consistent exposure to enterprise-scale fragmentation. He works at the point where AI strategy collides with platform reality. His depth in responsible AI governance is a practical differentiator; accountability is built into data infrastructure at the architecture stage, not appended as a compliance layer after the fact.

Michael Curry has spent 30 years inside enterprise systems, enough to have seen most modernization patterns fail. He leads the Data Modernization Business Unit across legacy data infrastructure, hybrid cloud, and AI readiness as a connected problem set. His IT modernization consulting is weighted toward regulated sectors: public agencies, industries where a compliance failure costs more than a delayed migration. He does not optimize for speed. He optimizes for systems that remain governed under sustained organizational pressure.

Alina Baranou is typically engaged at a particular inflection point: data exists in volume, but teams cannot agree on what it says. Her 15 years in data governance and BI modernization give her a specific angle. She treats the problem as architectural and restructures how data moves through cloud analytics platforms, where quality controls sit in the pipeline, and how access is governed so outputs stay consistent regardless of who pulls them.

Evans Tinga's background covers environments such as payment processing infrastructure, large-scale transport systems, and public-sector digital platforms. Thirteen years in that work produces a specific discipline: reliability is not traded against speed or elegance. His AWS specialization anchors the technical delivery, while an extension into AI-driven development reflects a broader focus, so development cycles stay controlled as the platform matures.

Andre Nix specializes in data platform modernization consulting at the infrastructure layer - where instability tends to follow workloads into the cloud. His 14-year focus on AWS and Azure has produced a methodology around infrastructure-as-code, CI/CD optimization, and serverless patterns that reduce fragility and cost. He works in hybrid environments where the gap between on-premises and cloud behavior creates reliability problems. Engagements are measured concretely: recovery time, spend reduction, platform predictability under load.

Yetish Narayana works with data platforms scaled faster than their architecture was designed to support, common where growth outpaced infrastructure planning. His 20-year record across AWS, GCP, and Azure covers the diagnostic side: locating where performance degrades, where allocation has drifted, and where early architectural decisions compound into problems at scale. Microservices restructuring and CI/CD optimization for high-volume workloads are consistent threads. Stability that persists as demand grows is the standard he works to.

Amer Raza's practice in data platform modernization consulting covers a technically demanding transition: environments moving from conventional cloud into AI-driven operating models. The shift compounds pressure: latency tightens, costs shift unpredictably, and model reliability introduces failure modes standard monitoring was not built to catch. His 20-year background spans multi-cloud architecture, DevSecOps, and AI systems engineering. As CTO and co-founder, he also shapes how teams take ownership of systems after the build.