In 2025, enterprise data engineering is not about how to store data or set up ETL. It’s about the ability to scale AI initiatives at the corporate level, synchronize hundreds of data sources in real time, comply with dozens of regulations, and at the same time keep infrastructure costs under control.
Companies that work with thousands of employees, customers, and big data need partners who can take on some of the architectural responsibility. And most importantly, work taking into account the specifics of the industry, legacy systems, internal SLAs, and security policies.
In this article, we have collected 15 companies that know how to work at the enterprise level. These are not just vendors with a good portfolio, but those that really meet the requirements of the Fortune 500 in terms of scalability, responsibility, and compatibility with the corporate ecosystem.
If your organization:
- has dozens of data sources, from CRM to industrial sensors;
- is actively implementing AI/ML or BI systems;
- is concerned about continuity, access control, and regulatory compliance;
this overview will save you weeks of research.
Strategic Importance of Data Engineering in the Enterprise Ecosystem
For midsize businesses, data is a tool. For large businesses, it's infrastructure. In 2025, data engineering will determine how quickly a company can launch a new AI model, integrate a third-party platform, perform a GDPR audit, or calculate financial risks in real time.
It all starts with engineering: how, where, when, and in what form data arrives. If pipelines can’t handle the load or if sources conflict, no BI system or LLM will save the day.
In addition to productivity, other aspects are also important for enterprises: continuity (data availability), manageability (data lineage, access control), security (encryption, governance), regulatory compliance (HIPAA, GDPR, SOC 2, etc.), and scalability (horizontal and vertical). Data engineering is the foundation on which all this is built.
Below are three key areas where the role of data engineering is especially critical for large businesses.
Driving Business Innovation Through Scalable Data Infrastructure
For a company to quickly introduce new products, automate processes, or adapt to market changes, it needs a stable and scalable data infrastructure capable of handling workloads that grow every month.
For an enterprise, this means:
- automatic scaling for high peak workloads (for example, in finance during the quarter closing);
- a flexible architecture that allows you to connect new sources without rebuilding the entire system;
- isolated environments for data experiments without the risk of breaking production;
- the ability to work with both cloud and on-prem solutions (especially relevant in regulated industries).
Supporting AI/ML and BI Systems at Scale
AI doesn’t work without quality data. BI doesn’t work without stable pipelines. But the problem in the enterprise isn’t that there’s no data. The problem is how to collect, clean, categorize, and present it in the right form—consistently, every hour, across all regions.
Machine learning models require:
- years of historical data (often from legacy systems);
- data reduced to uniform formats (feature consistency);
- the ability to retrain models on an updated dataset without the risk of losing control.
Business intelligence (Tableau, Power BI, Looker) integration for enterprise require:
- data updates in close to real time;
- aggregations and transformations at the data lake or warehouse level;
- stable latency regardless of volume.
A properly built data engineering infrastructure is what allows ML and BI teams to do what really brings profit: modeling, optimization, analytics.
Enabling Secure and Compliant Data Flows
For enterprise customers, it is critical that data pipelines:
- are able to accurately track where data comes from (data lineage);
- provide access control at the attribute level (RBAC, ABAC);
- support encryption both in transit and at rest (TLS, AES-256);
- allow for easy implementation of the right to erasure (GDPR) or segmentation of access by geography (data residency).
This is especially difficult to implement in hybrid architectures when part of the data is stored on-prem due to HIPAA or FedRAMP requirements, and the other part is stored in the public cloud.
Modern data engineering involves compliance not as an add-on later, but as part of the architecture from the very beginning. And here it is important to work with partners who have practical experience in bringing enterprise systems into compliance with SOC 2, ISO 27001, GDPR, HIPAA, PCI DSS, etc.
Enterprise-Specific Requirements for Data Engineering Partners
When choosing a global data engineering firm, the following requirements are important to consider:
- Integration with existing systems: a complete replacement of legacy s is usually impossible.
- Reliability and responsibility: SLA, 24/7 support and real cases with the Fortune 500 companies.
- The ability to work with hybrid clouds: part of the data always remains local.
- Industry experience: especially when it comes to finance, healthcare, telecom or retail.
In the following sections, we will consider these requirements in more detail.
Deep Integration with Legacy Systems
Most enterprises don’t have the luxury of starting from scratch. Data engineering teams have to work with ERP, MDM, CRM, and data repositories.
Typical situation: Salesforce, Oracle DB, several proprietary .NET services, internal API with limited documentation—and all this needs to be combined with a modern data lake in Snowflake or Databricks.
What is important here:
- support for non-standard connectors and API adapters;
- work with slow or transaction-sensitive sources;
- incremental data synchronization;
- transformation logic that takes into account the specifics of legacy structures (for example, classification codes, lack of timestamps).
Enterprise SLAs, Security & Support
Service Level Agreements (SLA) in the enterprise segment are needed for:
- clearly recorded metrics (uptime, latency, RTO/RPO);
- incident response within hours or minutes;
- guaranteed support channel (dedicated account manager, escalation paths);
- regular technical review sessions.
When working on engineering architecture, teams should take security requirements into account from the very beginning:
- encryption (in motion and at rest);
- access control at the role and attribute level;
- logging and monitoring of access events;
- compliance auditing (SOC 2, ISO 27001, GDPR, HIPAA, etc.).
And most importantly, technical support that:
- speaks the language of enterprise teams, not ticket system only;
- is capable of supporting an environment with thousands of pipelines;
- does not disappear after the MVP is implemented.
Experience with Large-Scale Cloud & Hybrid Architectures
Most enterprise companies today operate in a hybrid environment: some data is in the cloud (AWS, Azure, GCP), some is in local data centers or private environments due to security requirements or data residency policies.
What does this mean in practice?
- Data engineering must take into account latency between environments to avoid slow or unstable pipelines.
- The infrastructure must be able to scale horizontally to process tens of terabytes of data without manual intervention.
- Pipelines that work simultaneously with multiple cloud services are needed, taking into account the specifics of each (IAM policies, limits, billing).
- Systems must be fault-tolerant, especially if processing occurs across multiple regions or data centers.
Industry-Specific Expertise
Data engineering for enterprise is always about context. The same technology works completely differently when the domain changes: data structure, processing requirements, update frequency, reporting specifics, regulations. That is why the industry experience of the partner is a critical factor when choosing.
Different sectors have their specific needs and requirements:
Financial companies expect high accuracy, auditability, and often near real-time analytics.
Healthcare institutions work with sensitive personal data and tightly regulated flows.
Manufacturing businesses work with a large volume of IoT data and the need to integrate with SCADA, MES, ERP.
E-commerce deals with high traffic, dynamic catalogs and behavioral models.
Energy sector often uses time-series data with the requirement of 24/7 processing.
Telecom—with millions of simultaneous transactions and complex SLAs.
Large-scale data infrastructure providers need to understand how to customize data tools for a specific industry. That’s why in the next section we have selected those companies that combine technical expertise with a deep understanding of industry realities.
Top 15 Enterprise-Ready Data Engineering Companies in 2025
DATAFOREST
Worldwide offices: Kyiv, Ukraine; Lisboa, Portugal; Tallinn, Estonia.
Fortune 500 Clients: Amazon, eBay
Core Enterprise Services: Data Engineering, Custom Warehouse Automation, Advanced Planning Systems for Smarter Operations, Data Management and Analytics, BI, AI Agents for Financial Advisors.
Industries Served: E-commerce, retail, finance, traveltech, insurance, healthcare.
Why It’s Ideal for Enterprise Clients:
DATAFOREST is a leading data engineering company that works with enterprise clients like Amazon and eBay. They delve deep into the client’s business logic and tailor solutions to their specific processes. Their services for enterprises include full automation of warehouse logistics, supply planning using AI models, building custom agents and analytics for financial advisors, and others.
Their team works not only at the technical implementation level, but also as solution architects, taking into account data from ERP, CRM, and IoT sources. DATAFOREST is especially effective where enterprises need custom out-of-the-box approach, reduce planning time, and automate complex processes. Fill out a short form to connect for a call with their team.
Intelliarts
Worldwide offices: Lviv, Ukraine
Fortune 500 Clients: Dell Technologies
Core Enterprise Services: R&D, Technology Consulting, Data Science, Machine Learning, Artificial Intelligence, IoT, Big data Services
Industries Served: Manufacturing, Insurance, Renewable Energy, Agriculture, Digital Marketing.
Why It’s Ideal for Enterprise Clients:
Intelliarts specializes in complex data-driven projects—from building data platforms to embedded analytics at the enterprise scale. Their team has deep experience with AWS, Azure, and Snowflake and is able to adapt solutions to strict security requirements and SLAs.
InData Labs
Worldwide offices: Nicosia, Cyprus
Fortune 500 Clients: No
Core Services: Generative AI & GPT, Text Analysis, Cognitive Computing, Big Data Analytics for Enterprises
Industries Served: Finance, E-commerce, Marketing & Advertising, Manufacturing, Healthcare.
Why It’s Ideal for Enterprise Clients:
InData Labs is a leading AI company with deep experience in enterprise tech consulting, with a powerful R&D center and offices around the world. Their approach, a full cycle from AI strategy and PoC to production implementation and support, makes them the ideal choice for enterprises looking for a reliable AI service provider with world-class standards.
Addepto
Worldwide offices: Warsaw, Poland
Fortune 500 Clients: BMW, Nissan
Core Enterprise Services: Generative AI Development, Machine Learning, AI Discovery Workshop, LLMs Development, Data Migration Enterprise Solutions
Industries Served: Automotive, Aviation, Private Investments, Finance & Insurance, Manufacturing
Why It’s Ideal for Enterprise Clients:
Addepto is a strong partner for enterprises, especially in the automotive industry, looking for complete turnkey solutions. The team is able to build scalable analytical infrastructure, integrate AI into internal processes, and modernize legacy systems. Their work emphasizes a production-ready approach — taking into account data governance, SLA, and the cloud economy.
Azilen Technologies
Worldwide offices: Irving, USA
Fortune 500 Clients: No
Core Enterprise Services: Generative AI, Data Engineering Consulting, CloudOps, DevOps
Industries Served: HRTech, FinTech, RetailTech, InsurTech, Manufacturing.
Why It’s Ideal for Enterprise Clients:
Azilen Technologies is a prominent enterprise AI development company that provides AI, Data, and HRTech solutions to organizations seeking digital transformation. With a customer-centric approach, they create and nurture powerful partnerships to optimize end-to-end HRTech processes and revolutionize customer experiences through AI-based advancements.
Indium
Worldwide offices: Cupertino, USA
Fortune 500 Clients: Yes (NDA)
Core Enterprise Services: Big Data Engineering, Predictive Analytics, Machine Learning, Data Warehousing, Scalable Data Architecture Solutions
Industries Served: BFSI, Healthcare, Technology, Gaming, Retail, Manufacturing
Why It’s Ideal for Enterprise Clients:
Indium has strong expertise in implementing enterprise-grade data platforms and managing the full data lifecycle — from ingestion to production. The company actively applies the DataOps and ML Ops approach, which is especially valuable for enterprises with rapid releases and high requirements for quality and stability.
Adastra
Worldwide offices: Toronto, Canada; New York, USA
Fortune 500 Clients: Volkswagen, Toyota, MetLife, AstraZeneca
Core Enterprise Services: Artificial Intelligence, Data, Cloud, Digital and Governance services
Industries Served: Banking and Financial Services, Healthcare and Life Sciences, Pharmaceutical, Public Sector, Mining and Natural Resources, Manufacturing, Automotive
Why It’s Ideal for Enterprise Clients:
With over 20 years of experience, Adastra Corporation has been working with enterprises to transform data into a manageable, secure and analytically accessible infrastructure. Adastra delivers solutions to enterprises to leverage data that they can control and trust.
DataArt
Worldwide offices: New York, USA
Fortune 500 Clients: Priceline, Nasdaq
Core Enterprise Services: Custom Software Development, Financial Software Development, Mobile Application Development, Financial Services Software, Travel and Hospitality Software Solutions
Industries Served: Finance, Healthcare, CPG & Retail, Travel, Media, Education
Why It’s Ideal for Enterprise Clients:
DataArt is a global software engineering firm. DataArt is known for its ability to dive into complex domains and work as an extension of the client’s internal teams. They work on the principle of flexibility without compromise—important for enterprises with mixed infrastructures and dynamic needs.
Firebolt
Worldwide offices: Palo Alto, California, USA
Fortune 500 Clients: Yes (NDA)
Core Enterprise Services: Cloud data warehouse, big data analytics, low latency OLAP queries
Industries Served: SaaS, AdTech, E-commerce, Gaming
Why It’s Ideal for Enterprise Clients:
Firebolt is a next-generation cloud data warehouse for AI applications. Enterprises facing high-volume analytics workloads, millions of queries per day, and the need to respond in real time choose Firebolt because of its focus on speed and scalability.
K2view
Worldwide offices: Yokneam, Israel
Fortune 500 Clients: Vodafone, Sun Life, Verizon
Core Enterprise Services: Retrieval-Augmented Generation (RAG), Generative AI, GenAI, Enterprise AI, Synthetic Data Generation
Industries Served: Finance, Telco, Healthcare
Why It’s Ideal for Enterprise Clients:
K2view is a data product platform that gets customers’ data AI-ready: protected, complete, and accessible in a split-second.This platform is especially useful for enterprises that need rapid response, granular-level access control, and compliance with privacy regulations (GDPR, CCPA).
OpenText
Worldwide offices: Waterloo, Canada
Fortune 500 Clients: L'Oréal, RBC
Core Enterprise Services: Information Management, Enterprise Content Management
Industries Served: Manufacturing, Public Sector, Finance, Pharma
Why It’s Ideal for Enterprise Clients:
OpenText is a leading Cloud and AI company. Their solutions cover the full cycle: from collection and storage to compliance and automation. They are especially valuable for organizations with strict regulatory requirements, a branched data structure and the need to unify information flows between departments and regions.
HCLTech
Worldwide offices: Noida, India
Fortune 500 Clients: Verizon, State Farm
Core Enterprise Services: Data Engineering, Data Strategy, Cloud Solutions, Gen AI, Cloud Data Engineering for Corporations
Industries Served: Financial Services, Manufacturing, Life Sciences and Healthcare, Telecom, Retail and CPG, and Public Services
Why It’s Ideal for Enterprise Clients:
HCLTech is a global technology company, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. Strong in building cloud data platforms, developing data strategies, automating pipelines and providing real-time analytics.
PwC
Worldwide offices: London, UK
Fortune 500 Clients: Bank of America, ExxonMobil, IBM
Core Enterprise Services: Data engineering & Governance, Risk & Compliance Analytics, Data strategy, AI
Industries Served: Finance, Public Sector, Energy, Healthcare, Retail
Why It’s Ideal for Enterprise Clients:
PwC is a global company that combines consulting, technology and deep expertise in risk, compliance and business strategy. This makes them particularly valuable for enterprises that need to go beyond simply collecting or transforming data, and build systems that meet regulatory requirements, deliver real business value and scale across a global structure.
Accenture
Worldwide offices: Dublin, Ireland
Fortune 500 Clients: Walmart, Apple
Core Enterprise Services: Management Consulting, Systems Integration and Technology, Business Process Outsourcing, Infrastructure Outsourcing
Industries Served: Finance, Telecom, Healthcare, Automotive
Why It’s Ideal for Enterprise Clients:
Accenture is a leading global professional services company that provides services for the world’s leading businesses, governments and other organizations. Their data teams build scalable architectures that integrate dozens of sources, provide AI-ready infrastructure, and meet stringent regulatory requirements. Ideal for enterprises that need to scale quickly or modernize globally.
Intellias
Worldwide offices: Chicago, USA
Fortune 500 Clients: Yes (NDA)
Core Enterprise Services: AI/ML, Digital Transformation, Intelligent Automation, Product Engineering, Platform Development, Cloud, DevOps, Data & Analytics
Industries Served: Financial Services & Insurance, Retail, Healthcare, Telecom & Media, Travel & Hospitality, High-Tech
Why It’s Ideal for Enterprise Clients:
Intellias is a global software engineering and digital consulting company and a trusted technology partner to top-tier organizations that helps them accelerate their pace of sustainable digitalization. They work with large data sets and have experience in cloud modernization for enterprise clients with strict requirements for security and compliance.
Choosing the Right Enterprise Data Engineering Partner
Here are five key criteria to consider when choosing a data engineering partner for enterprises:
Industry Experience and Enterprise Clients
A team that has already worked with Fortune 500 companies knows what dozens of legacy systems, extensive management structures, technical debts and real risks in production environments are. Experience in a specific industry is another plus: finance, healthcare, energy or telecom have their own limitations, metrics and even deadlines.
Scalability and Cloud Architecture Expertise
Enterprises cannot afford a system that “works for now”. They need an architecture that can withstand business growth, analytics load, integration of new sources. It is important that the partner is able to design horizontally scalable solutions optimized for cloud environments, multi-regional deployments and hybrid cloud.
Security, Compliance & Data Governance
A tech partner must not only maintain compliance with standards like SOC 2, HIPAA, ISO 27001, GDPR but also have experience in embedding these requirements into the architecture: from access control to logging, from retention policies to the user rights model.
Ability to Integrate with Legacy Systems
In enterprises, it is almost always necessary to work with technologies that are 5-10 years old, often without full documentation, API or support. Therefore, tech vendors must have flexibility, attention to detail and the ability to work with limitations.
Long-Term Support and SLAs
Enterprises require 24/7 support, with clearly defined SLAs, response metrics, escalation levels, and clear roles on both sides. And, of course, strategic focus: the partner should understand your long-term goals, not just the scope of the current project.
Final Thoughts
Data engineering is the foundation of a modern enterprise ecosystem. Properly structured data provides not only transparency, but also a competitive advantage: in decision-making speed, in automation, in product personalization.
When choosing a partner, focus not only on technical expertise, but also on the ability to work with your context and build solutions that scale together with your business.
If you are interested in discussing how to adapt data engineering to your goals, the DATAFOREST team will be happy to conduct a short call and show you where to start.
FAQ
What are the key criteria for selecting a data engineering partner for an enterprise?
Experience with large companies, scalability of solutions, expertise in security and compliance, ability to integrate with legacy systems, reliable technical support and SLAs.
Which industries benefit most from advanced data engineering services?
Finance, healthcare, retail, manufacturing, telecom and energy, where fast analytics, data security and complex integration are most needed.
Can data engineering providers help modernize legacy data infrastructure?
Yes, this is one of the core competencies, including adapting legacy systems and migrating to cloud or hybrid environments.
What tech stacks do top enterprise data engineering companies commonly use?
Apache Spark, Kafka, Snowflake, dbt, Airflow, AWS/Azure/GCP, Kubernetes, Python, Scala.
What is the role of data engineering in enabling AI and advanced analytics?
Data engineering prepares, cleanses, and structures data, creating a robust foundation for AI models, real-time analytics, and large-scale experiments.
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