Global Data Engineering Companies That Dominate

The constant buzz surrounding generative AI and its potential value for organizations is making them rethink their approach to product development and internal operations. As data forms the backbone of AI, data engineering has become fundamental to every forward-looking organization.

Without access to clean, real-time data, AI opportunities slip through an organization’s fingers.

The growing demand for AI-ready capacity makes data engineering crucial in designing and maintaining the scalable infrastructure for AI-driven applications. According to McKinsey, By 2030, approximately 70% of total data center demand will be for AI-ready centers, with generative AI alone accounting for around 40% of that demand.

In this blog, we’ll explore what data engineering is, why it's crucial in today’s technology landscape, and the best firms to help your organization make the most of it.

What is data engineering?

Data engineering is the practice of designing and building systems that enable businesses to collect, store, transform, and analyze large volumes of data.

A result of data engineering is a well-designed infrastructure that makes data accessible to everyone within an organization, with respective access levels, enabling employees to use data to make informed decisions and do their jobs faster.

Fundamentals of data engineering lifecycle

Data engineers create and deploy data pipelines that bring data from multiple sources into a single storage. A data pipeline uses native integrations and APIs to aggregate disparate business data (data from different departments, tools, subsidiaries, etc.) across different data sources.

The next step is to store data in a storage. There are many options available today for data storage. Data engineers usually use data warehousing tools to create a single source of truth for gathered data.

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Note: The cost for data storage might range from $0.02 to $0.05+ per GB. The cost-efficiency of your data infrastructure relies heavily on the data warehouse you choose. Here you can find a list of top data warehousing tools to use in 2025.

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Data often arrives in storage in a raw format, with duplicate and redundant entries. So, the next logical step is to clean and normalize the dataset to prepare it for further use. Data engineers use various transformation tools, such as dbt to manipulate data and make it accurate and ready to drive insights for decision-making.

The final step is to ensure data is accessible to everyone in the organization. Data engineers prepare data for business intelligence tools and AI agents, enabling users to get actionable insights and make faster decisions. Clean data is also stored in a data warehouse, allowing everyone to use, analyze, and act on it as needed.

Data engineering can seem complex and it is – it requires expertise and precision. That’s why understanding its principles is key when deciding who to trust with your business’s data infrastructure. Without skilled data engineers or a software engineering partner, your business’s data could be inconsistent, messy or inaccessible, slowing down decision making and growth.

Here’s how data engineers describe their responsibilities themselves:

Why Businesses Need Data Engineering Solutions

To stay competitive in today’s digital world, businesses need systems to collect, clean, store and manage data. Whether it’s an SME with complex invoicing systems or a multinational with data from multiple locations, well defined data pipelines and processing solutions are a must. Without them critical information would be hard to access or entirely unusable.

Take a music streaming service for example. It couldn’t manage royalties or contracts without data engineering solutions to track song plays and payments. Thanks to modern data engineering these processes can now be automated, reducing manual intervention and making operations more efficient.

Data Engineering for Large Enterprises

In its 2022 forecast, McKinsey highlighted how data-driven enterprises are likely to operate in 2025, stressing the importance of data embedded in every decision and day-to-day workflow of employees.

By 2025, nearly all employees will use data naturally, solving problems quickly with innovative data techniques instead of lengthy roadmaps. Today, many organizations apply data-driven techniques only sporadically, leaving untapped potential and inefficiencies.

With a well-thought data engineering infrastructure companies can nurture data-driven culture that leads to improved performance, enhanced customer experiences, and the development of advanced new applications.

In reality, the state of data in organizations across different industries leaves much to be desired. A recent report by Hakkoda shows that only 29% of companies have a centralized storage for all of their data. Another 45% planned to centralize it in 2024. Since data centralization is one of the first steps in data infrastructure design, we can assume that more than 70% of organizations have immature or no data infrastructure at all.

Only 45% of companies report difficulties in making informed decisions with AI. This suggests that the rest of companies relying on AI without centralized data storage are likely using incomplete data, leading to flawed insights. This highlights the growing importance of data engineering and the risks that poor data infrastructure and data silos pose to organizations today.

Key data engineering opportunities for organizations include:

• Embedding data in every decision to eliminate guesswork
• Delivering real-time data to customers and employees.
• Using modern data engineering tools and techniques to reduce data storage costs and enhance performance.
• Sharing data across departments and organizations for better decision-making.
• Ensuring data is collected, stored, and processed following security regulations and best practices.

The Importance of Data Engineering for Startups

Data engineering is just as crucial, if not more so, for startups. Since the beginning of the AI boom, venture capitalists have become hesitant to fund companies or products that aren't AI-enabled.

For example, in Y Combinator's Summer 2024 batch, 211 out of 253 funded companies were AI-based or AI-enabled products—an impressive 83% of the batch.

Even though many of these startups use popular LLMs like GPT, the real value comes from data engineering:

• Feeding high-quality data into AI models, ensuring accurate and meaningful insights.
• Ensuring the data is clean and has the proper format so that AI can make predictions actionable for end users.
• Providing users with near real-time insights, speeding up routine operations and decision-making.

For AI startups, LLMs are often a black box, especially when using publicly available models. Startups can only make minor customizations, without full control over the model. The true value lies in the data infrastructure behind the product: how efficiently data is collected, how well it addresses the user's problem, and how securely it is stored and processed.

Data engineering is the backbone that supports these AI initiatives, helping startups provide practical value and stand out in a competitive landscape.

The Importance of Automation in Modern Industries

Automation is the backbone of many industries but it doesn’t work alone. Skilled data engineers – or more importantly the right software engineering firm – build the infrastructure that enables automation. Whether your business is streamlining supply chains, automating billing or using AI to deliver personalized customer experiences, the systems that enable automation must be designed, built and maintained by experts.

With the right software engineering partner your business can ensure data is not only available and accessible but also organized in a way that gives you actionable insights. This means your business can make faster and better decisions, giving you an edge in your industry.

Overview of the Data Engineering Market

With AI and automation on the rise across industries, the demand for data engineering services has skyrocketed. The global Big Data industry will reach over $103 billion by 2027. Despite this level of investment, many businesses are still struggling to find solutions that work for them. Less than 40% of businesses reported improvement in data collection, storage and analysis even after heavy spending. This is why choosing the right software engineering firm is crucial.

Why Skilled Data Engineering Firms are in Demand

As businesses generate more data than ever, the need for scalable data systems is growing. Businesses are moving their infrastructure to the cloud, with platforms like AWS, Google Cloud and Microsoft Azure offering data storage solutions that require complex pipelines and automation. And as AI and machine learning is becoming part of more workflows, businesses need experts to manage the massive datasets required for these technologies.

How Choosing the Right Data Engineering Company can Help your Business

Working with a data engineering firm can transform your business by automating manual processes, improving data quality and speed up decision making. Many businesses today rely on data from multiple sources – vendor databases, reports and data warehouses – to perform tasks like cost allocation. Without automation this requires manual effort and coordination across teams, potentially hundreds of hours a month.

With modern data engineering solutions these processes can be automated, data can be collected, processed and stored in central systems like data warehouses. This saves time, reduces errors and allows your team to focus on higher value tasks instead of manual repetitive work.

Top Data Engineering Companies to Consider

If you want to improve your business data management, working with a software engineering firm is essential. Companies like Deloitte, Vodworks and others build data pipelines, migrate to the cloud, automate workflows across finance, healthcare and technology industries. They have global expertise and can handle complex projects and help businesses of all sizes implement data engineering solutions that are scalable and reliable.

Below, we have gathered industry-leading companies across five countries to help you find the right data engineering vendor.

United Kingdom

Vodworks

Vodworks is a UK based software engineering firm with 12+ years of experience in software engineering and data engineering consulting.

The company hosts more than 200+ tech experts, with a large share of experienced data engineers, data architects, business intelligence analysts, and AI specialists. Vodworks' engineers are proficient with modern data stack and cutting edge tools like vector databases.

Below are some of notable projects delivered by the data engineering branch of Vodworks:

• The Vodworks team helped True Digital predict the movement and spread of COVID-19 pandemic using location data from over 30 million customers. The team optimized 5 trillion data points, reducing infrastructure costs by 50%.
• For EA Sports, Vodworks developed a resource management platform that centralizes global budgeting and resource allocation data. The platform includes predictive analytics, enabling accurate forecasting of resource needs for upcoming quarters. The project resulted in a 40% reduction in costs for EA's global resource management and allocations.

By choosing Vodworks' data engineering services you get:

• A clear project roadmap with guidance from the company's data engineers and architects.
• Access to a pool of 200+ experts in data warehouse solutions and end-to-end data pipeline engineering.
• Integration of your data storage with third-party solutions for analytics or AI/ML processes.
• Continuous coordination and support from the project management team during and after project implementation.

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Contact Vodworks team to consult data engineering experts and get an estimate of your project today.

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BJSS

BJSS is a well known technology consultancy that provides cloud and data engineering services to various industries including healthcare, finance and government. BJSS recently re-architected data pipelines for the NHS and reduced data processing time and improved patient care reporting. With annual revenue of over £200 million BJSS has won many awards including “Best Technology Consultancy” in the UK. They are known for modernising and automating data workflows.

Capgemini

Capgemini is a global consulting and technology services firm that offers data engineering services focused on data integration and transformation for large enterprises. Capgemini automated data workflows for an international bank and reduced data processing costs. With annual revenue of over €18 billion Capgemini is one of the top IT service providers in Europe and known for scalable data transformation solutions.

United States

XenonStack

XenonStack offers cloud native services, AI driven automation and big data analytics. Their data engineering services help businesses build real-time data pipelines and integrate AI solutions to optimize operations. Recently XenonStack partnered with several Fortune 500 companies to optimize their cloud environments and reduced operational costs by over 30% through automation of data workflows. With an annual revenue of over $50 million XenonStack is listed as “Top AI and Data Solutions Provider” by Gartner for its innovative and secure solutions.

Sigmoid

Sigmoid is good at data engineering, big data analytics and AI driven solutions that help companies optimize data workflows for real-time decision making. They recently automated supply chain analytics for a large retail client. Sigmoid is listed as “Top Data Engineering Firm” by G2 and has annual revenue of over $75 million and is a leader in data engineering.

Tech Genies

Tech Genies provides global IT services with data engineering solutions for various industries including telecom and healthcare. Tech Genies automated data reporting for a telecom giant, reduced manual processing time and improved data accuracy. They are listed in “Top 100 IT Service Providers” by Clutch and have annual revenue of around $50 million and growing with a focus on scalable data solutions.

Germany

Sovanta AG

Sovanta AG is known for its business intelligence and data engineering approach especially in SAP integration. Sovanta worked with a major German automotive company to build AI driven data pipelines that optimise production analytics and improved efficiency across manufacturing processes. With annual revenue of €80 million Sovanta is one of the top SAP service providers in Europe and provides transformative data solutions to industrial clients.

Lufthansa Systems

Lufthansa Systems provides data engineering services to the aviation industry focused on operations and customer service. Lufthansa Systems built an AI driven platform to optimise flight route planning and saved millions in fuel costs and improved operational efficiency. With annual revenue of around €500 million Lufthansa Systems is a top player in aviation technology and data engineering.

Codepan

Codepan is based in Berlin and focuses on AI and data engineering for industrial automation. They helped a large manufacturing client automate their supply chain data reporting system and reduce downtime. Codepan’s annual revenue of €20 million and being a rising star in the tech industry has got them recognition for their innovative solutions to modernise traditional manufacturing processes.

Japan

Rakuten

Rakuten is a leading e-commerce company and uses data engineering to optimise its platform for millions of transactions daily. Rakuten automated its marketing analytics pipeline and got real time customer insights that increased conversion rates. With annual revenue of over ¥1.4 trillion Rakuten’s data engineering expertise is key to their competitive edge in e-commerce.

Fujitsu

Fujitsu provides data engineering and cloud solutions for industrial applications and automates innovative factory data processes. Fujitsu helped a major electronics manufacturer automate production analytics and reduced waste and downtime. With annual revenue of ¥3.6 trillion Fujitsu is a global leader in industrial data engineering and cloud solutions.

Axelspace

Axelspace is a geospatial data and satellite imaging company and provides data engineering solutions for environmental monitoring and urban planning. Axelspace completed a project to automate satellite data collection for a Japanese government agency and helped monitor deforestation. With significant government contracts and being a leader in geospatial data Axelspace is expanding its presence in the global data engineering market.

South Korea

Samsung SDS

Samsung SDS provides IT services including data engineering and big data analytics to industries like manufacturing and finance. Samsung SDS automated its internal financial data reporting and reduced errors by 30% and saved millions in operational costs. Samsung SDS is one of the top IT service providers in South Korea with annual revenue of over $10 billion and is pushing the boundaries of data driven business optimisation.

LG CNS

LG CNS is a cloud based data engineering company and offers AI driven insights to industries like energy and electronics. LG CNS worked with a global electronics firm to automate its energy usage data collection and optimised efficiency and cut costs. With annual revenue of $3 billion LG CNS is a leader in data engineering especially in energy and is known for its innovative approach to sustainability.

Megazone Cloud

Megazone Cloud is South Korea’s largest cloud service provider and offers data engineering and analytics solutions for businesses moving to cloud. Megazone Cloud automated the data infrastructure of a major telecom company and reduced latency and improved data processing. With annual revenue of $200 million Megazone Cloud has been consistently ranked as a top cloud provider and is known for its cloud based data engineering expertise.

Canada

Deloitte Canada

Deloitte Canada provides data engineering services and specialises in cloud migration and data analytics for large enterprises in finance, healthcare and public services. Deloitte Canada worked with a provincial healthcare provider to automate patient data workflows and reduced processing time. With annual revenue of over CAD 3 billion Deloitte is known for its innovative approach to data solutions and has won numerous awards for its public sector IT projects.

Slalom Canada

Slalom Canada is a global consulting firm providing data engineering and cloud services and has a strong presence in Canada’s financial and retail sectors. Slalom automated the data pipelines of a large Canadian bank and improved operational efficiency and reduced manual processing time by 90%. Slalom’s annual revenue is over CAD 1 billion and is one of the top IT consultancies in North America for its innovative and client centric data solutions.

ThinkData Works

ThinkData Works is based in Toronto and provides data aggregation and engineering services to industries like government, finance and healthcare. They worked with the Canadian government to automate real time data collection for public health monitoring and improved response time and data accuracy. With annual revenue of over CAD 20 million ThinkData Works is highly rated on Clutch and is one of Canada’s leading data startups.

How Can Vodworks Help You?

Vodworks is one of the top software engineering companies globally and provides world class data engineering and software solutions to businesses of all sizes. With expertise across multiple industries from media and entertainment to enterprise solutions Vodworks delivers precision and innovation to its clients. Vodworks is your go to partner for software engineering services to grow and operate. Get in touch with Vodworks today for a consultation.