Book a flight through a major travel platform, place a wager on a betting exchange, or sit for a clinical trial, and there is a decent chance the software underneath was built by a company you have never heard of. DataArt has spent nearly thirty years as the engineering department that other companies rent - and it has grown to roughly 6,000 people almost entirely out of view.
Founded in New York in 1997 by Eugene Goland, DataArt started with an idea that sounds obvious now and was faintly radical then: the best engineers are not all in one place, and a company can be built to find them, keep them, and point them at hard problems. Goland had already co-founded Mail.ru, at the time the largest web portal in Eastern Europe. He built DataArt with a distributed delivery model from day one, years before the collaboration tools most remote teams now take for granted existed.
That head start matters to the story. When DataArt began stitching together teams across the United States and Eastern Europe, it was solving the coordination problem - trust across time zones, shared standards, code review at a distance - through habit rather than software. The dot-com startups it served in its first years needed capacity fast, and the firm's answer was to treat geography as a feature rather than an obstacle. Decades later, when the rest of the industry rediscovered distributed work, DataArt had already been living inside it for a generation.
A rented engineering department for regulated industries
DataArt describes itself as a global software engineering firm and technology consultancy. In practice that means designing and building custom software: data platforms and pipelines, cloud migrations, legacy-system modernization, mobile and web products, security testing, and increasingly artificial intelligence. The common thread is that the work tends to land in industries where mistakes are expensive and rules are strict.
The company concentrates on five verticals - finance, travel and hospitality, healthcare and life sciences, media and entertainment, and retail - rather than trying to serve everyone. That focus is deliberate. A firm that has spent years inside trading systems and booking engines knows the regulations, the failure modes, and the vocabulary, which is hard for a generalist competitor to replicate on a first engagement.
Names you know, on projects you don't see
DataArt's client list reads like a tour of the industries it targets: Nasdaq and S&P in finance, Travelport and Priceline in travel, Ocado in grocery technology, Legal & General in insurance, artnet in the art market, and Betfair and Flutter Entertainment in gaming and betting. These are large enterprises, not consumers, and the engagements are usually long. Some client relationships run for many years, which is the point of the model - DataArt sells continuity, not a quick contractor drop-in.
The range is part of what makes the firm hard to summarize. The same company that builds risk and trading tools for a stock exchange also works on the systems that price a hotel room, route a grocery order, or track a bet in real time. Each of those problems has its own regulator, its own tolerance for latency, and its own definition of a catastrophic bug. DataArt's pitch is that it has already learned those rules the expensive way, on someone else's project, so a new client does not have to.
“Business models need to be new, and core principles need to be focused on how you treat people.” Eugene Goland, President & CEO
Building things banks and hospitals can trust
Enterprises in regulated sectors face a specific bind. They need modern software - real-time analytics, cloud infrastructure, AI features - but they cannot afford outages, data leaks, or audit failures, and they rarely have enough senior engineers on staff to build it all. DataArt's job is to supply that engineering capacity along with the domain knowledge to use it safely: migrating a legacy platform to the cloud without breaking it, standing up a data pipeline that a compliance team will sign off on, or modernizing a system that has been running critical operations for a decade.
Retention as a competitive strategy
Plenty of firms offer offshore or nearshore engineering. What DataArt talks about most is not price - it is people. The company lists four values: people first, expertise, flexibility, and trust. Goland has argued that these are not decoration but the actual competitive advantage, the reason the firm could hold onto engineers through periods when everyone was poaching talent. A consultancy whose people stay can offer clients teams that stay too, and that continuity is genuinely hard for a lower-cost competitor to match.
That philosophy was tested in June 2022, when DataArt exited the Russian market following the invasion of Ukraine, relocating people rather than keeping the operation running. The firm kept growing afterward, expanding through acquisitions in Armenia, Romania, and beyond. For a company whose whole model rests on keeping engineers, a values-versus-revenue decision of that scale is a real stress test, and it chose to move people.
The recognition tends to follow the same theme. DataArt was named to Newsweek's Top 100 Global Most Loved Workplaces in 2023, recognized as a leader in the IAOP Global Outsourcing 100 in 2024, and named Best Global Software Engineering Company - US at the 2024 Technology Innovator Awards. Awards are easy to over-read, but the pattern - workplace lists alongside outsourcing and engineering awards - fits a firm that treats its talent pool and its delivery quality as the same asset.
From data pipelines to governed AI
The core service lines are data and analytics, AI and machine learning, product development and engineering, cloud migration, legacy modernization, and managed support with security testing. In 2024 the company added an Advanced AI Strategy Consulting service. In 2025 it went further, committing $100M to scale its data and AI capabilities and launching Artisyn, an AI-enabled operating model.
Artisyn is the most interesting piece, because it addresses the part of the AI wave that gets less attention: governance. It combines intelligent agents, reusable foundations, and enterprise controls so that AI can be used inside regulated workflows - clinical trials, financial services - while keeping every step auditable. According to the company, Artisyn automates repeatable setup, testing, and delivery work so engineers can focus on architecture and problem-solving. DataArt reports internal figures of up to 70% faster prototyping and roughly 15% engineering cost reductions in some engagements. Those are company-reported approximations, but the direction is clear: use AI to speed delivery without giving up the paper trail.
Selling teams, not tickets
DataArt is a business-to-business services company. Revenue comes from custom software projects and, more distinctively, long-term dedicated engineering teams embedded with a client - a model built around multi-year relationships and deep domain expertise rather than one-off staff augmentation. It is not positioned as the cheapest option; it is positioned as the firm that already understands your industry and will still be there next year. Estimated annual revenue sits near $793.8M, and the firm has appeared on the Inc. 5000 list twelve times, ranking among the top 0.1% of companies to do so.
The quiet tier of global engineering firms
DataArt sits in the same competitive neighborhood as EPAM Systems, Luxoft, Endava, Grid Dynamics, SoftServe, GlobalLogic, and Thoughtworks - global software engineering firms that build for enterprises rather than sell their own product. Within that group DataArt is smaller and less famous than the biggest players, but its vertical focus and its retention-first culture are how it distinguishes itself. It competes on knowing a handful of industries very well and on keeping the people who know them.
The technology footprint underneath all of this is deliberately broad. DataArt's engineers work across the major clouds - AWS, Azure, and Google Cloud - and across the modern data and AI stack, from Databricks and Snowflake to the current generation of GenAI tooling. That breadth is a requirement, not a boast: a firm that agrees to modernize whatever a client already runs cannot afford to be a one-platform shop. The trade-off is that DataArt rarely gets to standardize; every engagement starts with someone else's legacy system and someone else's constraints.
Where the firm goes next looks like a continuation rather than a pivot. The $100M data and AI commitment, the Artisyn operating model, the AI strategy consulting practice, and a multi-year AI research agreement with London Business School all point in the same direction: making AI usable inside the regulated, audit-heavy environments DataArt already serves. That is a narrower ambition than replacing engineers with agents, and a more defensible one. The bet is that in finance and healthcare, the winning version of AI is the one that can show its work.
Nearly three decades, briefly
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Figures are drawn from public sources and company statements; revenue and internal performance numbers are approximate. Last updated August 2026.