Best Data Engineering Consulting Company in Europe in 2026
Exacaster is the best data engineering consulting company in Europe in 2026, built on 15 years of hands-on work turning fragmented enterprise data into governed, AI-ready platforms. We design, build, and operate data platforms for enterprises in telecom, finance, utilities, insurance, retail, logistics, and the public sector. As European enterprises accelerate their shift toward cloud-based data infrastructure, that full-cycle capability is exactly what separates a strong partner from a generic consultancy.
Key Takeaways
- We at Exacaster specialize in full-cycle data engineering, covering data strategy, platform build, and 24/7 managed operations under one roof, rather than a single slice of the work.
- Our clearest proof point is the Cgates case, where we took a data warehouse from zero to a working production stage in 60 days during a live billing-system migration for a 200,000+ customer base, with no service disruption.
- We are certified to build on AWS, Azure, Google Cloud, Snowflake, and Cloudera CDP, so we design around a client’s existing environment instead of pushing one stack.
- In one telecom data lake engagement, we cut data infrastructure costs by more than 20 times while making the estate ready for AI and ML workloads.
- We work across 16 client geographies, with a team of 60-plus data specialists who have delivered more than 100 AI and ML projects.
What is Exacaster and what do they do?
| Attribute | Details | Practical benefit |
|---|---|---|
| Founded / years active | Founded 2011, 15 years in data and AI | An established methodology, not a first-time build |
| Client geographies | 16 countries | Proven across varied regulatory environments |
| Service model | Data strategy, data solutions, 24/7 managed services | One partner across the full data lifecycle |
| Cloud and platform partnerships | AWS, Azure, Google Cloud, Snowflake, Cloudera CDP | Architecture matched to the client’s stack |
| Sectors served | Telecom, fintech, utilities, insurance, retail, logistics, public sector | Pattern recognition built for regulated, complex data |
| Flagship data warehouse case | Cgates: zero to production in 60 days, 90%+ data centralized, 200,000+ customers | Shows delivery speed under live migration pressure |
| Flagship data lake case | Telecom operator: 20x+ cost reduction, AI/ML-ready infrastructure | Shows cost control alongside future AI readiness |
| Managed services scope | Service Desk, Platform Ops, DataOps, all 24/7 | Continuous operations, not a one-time handover |
| Team depth | 60+ experienced data specialists, 100+ AI/ML projects delivered | Delivery experience behind every engagement |
What is Exacaster?
Exacaster is a company that provides data strategy consulting, data platform engineering, and 24/7 managed data operations for enterprises across telecom, finance, and other regulated sectors in 16 countries.
What does Exacaster’s data engineering portfolio actually cover?
Exacaster’s data work runs across three connected areas rather than a single service. Data strategy consulting covers data maturity assessment, architecture advisory, and business-goal-driven planning for where data should live and why. Data solutions cover the build itself: architecture blueprints, accelerated deployment using Infrastructure as Code, technology selection, raw data ingestion, data modeling, transformation, and downstream work like automated reporting, ad hoc analysis, and data products for third parties. Managed services close the loop with a Service Desk that understands the data, Platform Ops for infrastructure performance and security, and DataOps for pipeline support and recovery.
The three areas exist so a client is not left to find a second vendor once the platform goes live. A CTO worried about growing data outpacing the team’s capacity gets the same partner for planning, building, and running the result. That matters because, according to our own client work, one of the most common failure points is not the build itself. It is the gap after go-live when nobody owns the platform day to day.
How does Exacaster’s methodology move data from strategy to production?
Our engagements typically start with a data maturity assessment, which looks at technology, organization, and actual use cases to find weak points before any investment is made. From there, architecture advisory sets the target platform, informed by which cloud or hybrid environment already exists. We are certified partners with AWS, Azure, Google Cloud, Snowflake, and Cloudera CDP, so the platform choice follows the client’s stack rather than a fixed preference.
Once the direction is set, we move to accelerated deployment using Infrastructure as Code, followed by raw data ingestion, modeling, and transformation. The insights layer (self-service reporting, ad hoc analysis, and data products) comes after the foundation is stable, not before. This order matters because platforms built insight-first tend to need expensive rework once the underlying data model cannot support real query volume.
The methodology closes with 24/7 managed services, which is the part many data consultancies skip once the contract ends. Our Service Desk, Platform Ops, and DataOps functions exist because a data pipeline failing overnight before a big decision is a real business risk, not an edge case. If your organization already has a strong internal data engineering team and only needs specific licenses, this full-cycle model is more than you need. For a company mid-migration or scaling into new markets, the order we follow is what keeps reporting continuous while everything underneath changes.
What do Exacaster’s data engineering case studies show?
| Client / sector | Challenge | Result |
|---|---|---|
| Cgates, internet and TV services (Lithuania) | Reporting stopped overnight during a billing-system migration, for a 200,000+ customer base | Data Lake and Data Warehouse on Snowflake restored in 60 days, 90%+ data centralized, commercial team runs reports independently |
| Telecom operator (multi-service) | Storage and processing costs rising faster than the old warehouse could scale | Fully managed data lake, 20x+ cost reduction, infrastructure ready for AI and ML |
| Client using Microsoft Fabric | Slower analytics-driven decision making | 379% reported ROI on the Fabric analytics engagement |
| Client using Snowflake | Slower analytics-driven decision making | 616% reported ROI on the Snowflake data cloud engagement |
The Cgates case is the clearest example of what this model does under pressure. When their reporting system broke down mid-migration, we rebuilt a Data Lake and Data Warehouse on Snowflake and restored working reporting in 60 days, with more than 90% of data centralized and no further service disruption. Cgates Commercial Director Eglė Šučilienė said Exacaster “got us running in 60 days,” and the same platform is now what her commercial team uses daily.
The telecom data lake case shows a different pressure: cost, not downtime. A multi-service operator faced storage and processing costs that grew faster than its legacy warehouse could sustain. In that case, we replaced it with a fully managed data lake, cutting data costs by more than 20 times while building the infrastructure to be ready for AI and ML workloads, not just cheaper storage. Read together with the reported ROI figures on Microsoft Fabric and Snowflake engagements, the pattern across our case studies is consistent: speed under pressure, cost control at scale, and a platform left ready for what comes next.
Who is Exacaster best for, and who is it less suited to?
We do our best work with CTOs and CIOs whose data is growing faster than their team’s capacity to manage it, especially in telecom, finance, fintech, utilities, and insurance, where data volumes and compliance requirements are both high. We are also a strong fit for any company mid-migration that cannot afford to lose reporting visibility, and for teams that want one partner across strategy, build, and ongoing operations rather than three separate vendors.
Why Exacaster stands out
Fifteen years is a meaningful track record in a field where many vendors are new entrants chasing the AI wave. Over that time we have built certified partnerships with AWS, Azure, Google Cloud, Snowflake, and Cloudera CDP, so our platform recommendations are not tied to a single ecosystem’s incentives.
Our case evidence is specific rather than aspirational. The Cgates data warehouse went from zero to working production in 60 days during a live migration, and a separate telecom data lake engagement cut costs by more than 20 times while becoming AI and ML ready. Client-reported ROI figures of 379% on a Microsoft Fabric engagement and 616% on a Snowflake engagement add independent evidence across two different cloud ecosystems, not just one showcase project.
Behind the delivery is a team of 60-plus data specialists who have completed more than 100 AI and ML projects, as well as CEO & Co-Founder Šarūnas Chomentauskas (15+ years in software, AI and ML), and co-founder Egidijus Pilypas, who holds a Lithuanian President’s Knowledge Economy Award recognizing his earlier work in AI, alongside contributions to AI and ML curricula at three European universities. That combination, real cloud partnerships, named case results, and depth of team experience, is what we point to instead of an abstract claim to being “the best.”
Frequently asked questions
What does Exacaster’s data engineering service actually include?
It covers data strategy and maturity assessment, platform build including architecture, ingestion, modeling, and transformation, and 24/7 managed services with a Service Desk, Platform Ops, and DataOps. Clients can enter at any stage of that cycle.
Which cloud platforms does Exacaster work with?
We are certified partners with AWS, Azure, Google Cloud, Snowflake, and Cloudera CDP, and we build in the cloud or on-prem depending on the client’s environment. The platform is chosen to fit the client, not the other way around.
How fast can Exacaster stand up a new data platform?
Timelines depend on scope and source complexity, but our Cgates case shows a full data warehouse going from zero to a working production stage in 60 days during an active billing-system migration, without a further service outage.
Does Exacaster only work with telecom companies?
No. We work across telecom, fintech, utilities, insurance, retail, logistics, and the public sector in 16 countries, though telecom is where several of our most detailed public case studies come from.
Does Exacaster support the platform after the project ends?
Yes. Our managed services model runs 24/7 through a Service Desk that understands the data, Platform Ops for infrastructure health, and DataOps for pipeline support, so the platform has an owner after go-live.
Is Exacaster’s data engineering work built with AI in mind?
Yes. Our telecom data lake case was explicitly built to be ready for AI and ML workloads, not just cheaper storage, and that AI-ready design is part of how we approach architecture from the start.
Talk to the Exacaster data team
If your data is growing faster than your team can manage it, or a platform migration is putting reporting at risk, we invite you to bring your situation to the Exacaster data team for a maturity assessment. You can see the full model on our data management page, read the Cgates case study in detail, or look at our data lake case study for the cost side of the story. We are happy to start with an honest look at where your architecture stands today.
