11 Application Modernization Consultants and Experts Worth Following in 2026

Igor Omelianchuk leads a modernization company and steps in when legacy data turns unreliable yet sits too deep to replace. His clients come to him facing the same tangle: scattered data, ownership no one will claim, reports that disagree, broken integrations tipping into data chaos, and legacy systems that cost too much to keep supporting. As CEO and an application modernization consultant, he rebuilds flows, steadies architecture, and moves ETL/ELT pipelines to the cloud, where it pays. Across 15+ years and 30+ modernization projects that he managed himself, his rule holds: modernize without breaking what the business runs on.

Andrew Lychuk reads software modernization consulting as an architectural question first, a pipeline question second. He is usually engaged when modernization reaches past pipelines into surrounding systems. 18+ years across legacy data, infrastructure, and delivery models shape how he draws the line between what should move and what should stay, then maps how data flows shift without opening new risk. His multi-cloud planning stays practical: measurable outcomes over tidier diagrams.

Christopher Etheridge's focus on the regulated sectors: data platforms that run at scale within FedRAMP, CMMC, and ITAR boundaries. Across 20 years, he has met the familiar failure modes, lagging reports, hours-long pipelines, compliance drag, and rebuilt around them. Working primarily in AWS GovCloud and Databricks, he reshapes architectures so compliance is structural, not retrofitted. The gains are measurable: pipelines in seconds, audit-ready systems, data ready for analytics and AI.

Andre Nix takes on app modernization consulting where most migrations stumble: the system moves to the cloud, and instability moves with it. A senior cloud architect, he closes the gap with IaC in Terraform and CloudFormation, tightens CI/CD, and uses serverless patterns that buy reliability and cost control. His results read in numbers: faster recovery, lower spend, sturdier load handling. After 14 years, his signature is platforms that behave predictably, not react to every spike.

Scot Carlson works at the seam where AI ambition runs into fragmented reality, a data ecosystem grown too tangled to trust. Sixteen years of enterprise transformation, plus engagements at Deloitte, Samsung, WPP, and Publicis, gave him exposure to fragmentation at scale. He pulls data, platforms, and operating models into something usable. His responsible AI governance treats accountability as an architectural decision, not an afterthought, so what he builds stays governed as complexity climbs.

Thirty years in enterprise systems show in how Michael Curry works: he favors practical choices over conceptual models. As President of a Data Modernization Business Unit, he connects data modernization, hybrid cloud, and AI into one operating model, not disconnected projects. His software modernization consulting rebuilds data foundations to scale, integrate, and stay compliant under load, with DevSecOps built in, not bolted on. His scorecard: usable data, stable platforms, efficiency that shows.

Sebastian Frassia concentrates on the operational side of modernization, which decides if a rebuild runs. His focus is governance, team alignment, and keeping data flows consistent as systems scale across distributed, real-time stacks. His AI and automation background pushes him to build platforms that inform decisions, not just store records. And he ties engineering and business choices to one set of measures: data quality, cost, reliability, so the work yields real change, not marginal gains.

Alina Baranou is typically engaged when an organization holds data in abundance but can rely on little of it: dashboards conflict, and decisions stall during reconciliation. As Chief Data Officer, she aligns data strategy with modern architecture, breaks the silos that keep numbers inconsistent, and stands up cloud analytics that hold under real conditions. She treats data quality as the foundation, not a cleanup task. What clients notice first is quiet: teams stop second-guessing the figures and simply move.

Evans Tinga brings an AWS specialist's discipline to cloud and data systems: structured, outcome-driven, measured against what ships. Over a decade, he has run large-scale migrations across payment and transport platforms, where reliability under pressure is the whole job. Lately, he has carried that into AI-driven development, shaping how systems move from architecture to working software. His instinct runs toward delivery: an elegant design stays unfinished until it holds up in production.

Amer Raza works the transition that catches teams off guard: moving from conventional cloud into AI-driven environments, where latency, cost, and model reliability pull at once. As a CTO and co-founder, he pairs infrastructure depth with hands-on AI engineering, MLOps, AIOps, and LLM platforms on Kubernetes. His grounding in multi-cloud, IaC, and DevSecOps lets systems scale without losing control. Twenty years in, his test is operational: platforms a team can run, not just stand up.

When data platforms grow faster than anyone structures them, Yetish Narayana works to restore balance. Two decades across multi-cloud transformations and analytics under load taught him where strain shows: performance bottlenecks, wasted resources, the architectural drift that creeps into scale. He redesigns how systems deploy and scale, with microservices and disciplined cloud governance. He optimizes for stability over raw speed, building platforms that hold as demand and complexity climb.