Big data innovation in Europe doesn’t come from flashy dashboards or clever branding. It comes from the companies building the infrastructure underneath: the pipelines, the warehouses, the governance layers, the models that don’t break when the real world gets messy.
And as organisations across the continent race toward automation and AI adoption, data engineering has become the quiet backbone of every transformation.
The firms below represent some of the most reliable data engineering partners in Europe — teams known for turning scattered, inconsistent information into something decision-ready.
Whether you’re preparing for cloud migration, real-time analytics, or full-scale AI initiatives, these companies are shaping the data landscape in very real, very practical ways.
1. CHI Software — Practical, Scalable Data Platforms for Fast-Growing Businesses
CHI Software leads this list for one simple reason: their engineering teams don’t treat data work as isolated pipeline building. They approach it as a full ecosystem.
Their data engineering services cover everything from architecture design and warehouse development to ETL/ELT processes, big data platforms, and data quality frameworks — all tailored to the way each company actually operates.
Before any implementation starts, CHI Software digs deep into a client’s data reality: source systems, gaps, undocumented workflows, and cloud maturity. This upfront clarity allows them to propose architectures that scale instead of collapsing after six months.
Key capabilities often include:
- Data architecture design and warehouse planning;
- ETL/ELT pipelines across diverse structured and unstructured sources;
- Big data processing setups for high-volume workloads;
- Systems that prepare organisations for analytics, BI, and AI adoption.
These strengths benefit businesses that need to unify fragmented systems, support fast growth, and move toward real-time decision-making without blowing up existing operations. Teams often highlight cooperation as structured, transparent, and highly technical — the kind of partnership where data starts working for the business rather than slowing it down.
2. Netguru — Data Engineering Tightly Connected to Product and AI
Netguru approaches data engineering with a strong product mindset. Companies that work with them appreciate how closely their engineers align infrastructure with real business use cases — recommendation systems, forecasting engines, personalization layers, operational analytics.
Before diving into the technical layer, Netguru works to understand how data flows through the entire digital environment. That means identifying where automation is blocked, where data bottlenecks form, and where analytics could materially improve user experience or operational speed.
Their expertise typically includes:
- Real-time data streaming architectures;
- Cloud data warehouses integrated with applications;
- Automated pipelines for machine-learning-ready datasets;
- Event-driven systems for timely insights and alerts.
This analytical yet product-oriented approach makes Netguru a strong fit for teams scaling digital platforms and seeking a data foundation that can power both analytics and user-facing features.
3. DataArt — Enterprise-Grade Engineering for Complex Data Environments
While some data engineering companies excel at quick wins, DataArt is known for mature, long-term transformations. They specialize in complex enterprise environments where systems have been evolving for years — or decades — and where modernization requires precision rather than disruption.
DataArt typically helps organisations clarify their data landscape first, then introduce structure through governance frameworks, scalable architectures, and cleaner engineering practices. It’s an approach geared toward stability.
Their work commonly includes:
- Designing enterprise data architectures;
- Building governed pipelines for analytics and BI;
- Improving data consistency across business units;
- Supporting migration from legacy systems to cloud or hybrid models.
These strengths appeal to enterprises with strict compliance needs and multilayered systems — industries like finance, healthcare, and manufacturing, where accuracy and traceability matter as much as performance.
4. DevsData — Boutique Engineering with a Focus on Performance and Analytics
DevsData operates differently from the larger European consultancies. They position themselves as a boutique engineering partner with deep experience in big data platforms and machine-learning-driven analytics.
Clients often choose them for high-complexity, high-impact projects that require close collaboration with senior engineers rather than a large delivery structure.
Before building anything, their teams audit existing pipelines, storage layers, and data models to locate inefficiencies — performance bottlenecks, redundant transformations, and unnecessary reprocessing.
Their strongest areas include:
- Big data architectures capable of handling rapid growth;
- Pipelines optimized for ML workloads;
- Real-time analytics systems tailored for business operations;
- Detailed cost-performance optimization for cloud environments.
This hands-on, performance-centric approach works well for organisations that care about speed, efficiency, and precise control over their data ecosystems.
5. STX Next — Python-Driven Data Engineering Integrated with Product Backends
STX Next brings a very engineering-first view to data projects, rooted in their Python expertise and strong background in backend development. That means they’re especially good when data engineering is closely intertwined with operational systems — SaaS platforms, custom business applications, APIs, or microservices.
Rather than building a completely isolated data environment, they create pipelines and models that sit cleanly within existing technical stacks, ensuring smooth communication and maintainability.
Their capabilities typically span:
- Custom ETL/ELT development using Python;
- Data integration from multiple operational sources;
- Performance tuning and pipeline optimization;
- Tooling that supports analytics and predictive models.
Companies often choose STX Next when they need data engineers who also understand software architecture deeply — not just the analytics layer.
Choosing the Right European Data Engineering Partner
All five companies on this list excel at building solid data foundations, but choosing the right partner depends on your context as much as their capabilities.
- If your organisation needs practical, scalable, and AI-ready infrastructure, CHI Software provides a strong balance of architecture, engineering, and long-term support.
- If your product roadmap leans heavily on AI, automation, and user-facing insights, Netguru’s product-aligned engineering is a natural match.
- For legacy-heavy, multi-system enterprises, DataArt offers reliability, governance, and architectural clarity.
- Companies that want high-touch, performance-driven engineering often gravitate toward DevsData.
- And when data engineering must be integrated deeply into existing applications and backend logic, STX Next brings the right technical perspective.
Significant data innovation isn’t really about big data anymore — it’s about whether your systems can keep up with the decisions your organisation needs to make.
The best engineering partners are those who create the foundations that let analytics, automation, and AI actually thrive.
