As the pace of doing business in the AI era continues to accelerate, and customer demands keep growing, many organisations are discovering that the systems they use can’t keep up with modern expectations. The data foundations they have laid aren’t enough to support the skyscraper they’re trying to build.
This is because modern operations rely on timely data. Whether it’s fraud detection, predictive maintenance, dynamic pricing, or strategic decision-making, every second counts. The most valuable business outcomes depend on a continuous stream of context-rich information.
Unfortunately, organisations have historically relied on batch-based systems, moving data from source systems into centralised warehouses where it is collected, stored, and processed at scheduled intervals. There was a time in which banks would only know what had happened the day before when they’d completed batch processing overnight!
These systems were suitable for reporting, dashboards, and traditional business intelligence, but for today’s pace of business, batch just doesn’t cut it. Delays in data processing can result in slow decision-making, where in sectors like finance, healthcare, logistics, and retail, even seconds can lead to lost revenue, poor user experience, or compliance failures.
While cost-effective in the past, batch processing is no longer sufficient to support the decisions that need to be made instantly. It’s the difference between reacting today and reacting tomorrow.
Meet the data streaming engineer
In response to this shift, a new technical role is quietly gaining prominence: the data streaming engineer.
This specialist role is focused on building and maintaining data pipelines that allow information to move continuously with low latency across systems and applications. Typically based on event-driven, stream processing systems like Apache Kafka and Flink, they deliver an architecture in which each individual data point is analysed or contextualised in real-time — crucial for everything from fraud detection to dynamic pricing.
While the title may still be unfamiliar in some boardrooms, its importance is becoming increasingly evident. Some of the biggest brands in the world have adopted a data streaming architecture in order to be able to treat data as a living, moving asset rather than something stored for “later.”
For example, many transportation services rely on data streaming for arrival times, pricing, and fraud checks, which is central to customer experience — and it may provide the real-time infrastructure required for autonomous taxis to be launched in the UK.
Why it matters now
While data streaming offers a realistic, cost-effective solution for businesses that need real-time data, it’s another thing to have the skills to implement it. Recent studies show the gap clearly: 93% of UK data engineers believe real-time streaming is vital for scaling, yet 68% say their teams currently lack the necessary expertise.
While many have invested in data scientists and AI talent, these roles aren’t necessarily able or instructed to transform the infrastructure of the business — and without strong, low-latency data infrastructure, those skills can’t deliver their full value.
As a result, the demand for data streaming engineers is rising fast, with early adopters more capable of responding to change, personalising customer experiences, and unlocking greater and more sophisticated automation.
Building real-time readiness
Across industries, the opportunities are multiplying. Banks are exploring streaming for real-time risk scoring and transaction monitoring. Retailers are optimising supply chains with live inventory tracking and in-the-moment promotions. Healthcare providers are using streaming to patient monitoring and clinical decision support.
The emergence of the data streaming engineer reflects a broader shift in how enterprises must approach talent. Without robust, scalable, real-time data infrastructure — and the talent to build, manage, and develop it — business operations can slow down and miss opportunities. That means reskilling existing engineers, hiring specialists in real-time systems, and creating cross-functional teams that understand how to turn streaming data into business value.
Organisations must also examine their internal culture. Are teams structured to collaborate across data, engineering and AI? Can decision-making processes adapt to insights delivered in milliseconds, rather than hours or days? Is the business prepared to act on real-time signals instead of waiting for retrospective analysis?
These questions are now at the heart of what it means to be truly ready for the future. The data streaming engineer is fast becoming central to answering them.
As real-time data becomes the lifeblood of business, companies that embrace this shift will be best placed to turn technological potential into real-world results. The engineers who power that transformation and those who ensure data is always available, and always trustworthy — will be building more than pipelines. They’ll be building the future.
