Today, more and more companies rely on data and AI to scale their operations. According to recent statistics, 75% of all organizations will soon utilize AI and machine learning technologies with data engineering. That's why data engineering is more critical than ever before, and businesses that don't hire data engineers will fall behind.
Data engineering is often misunderstood or undervalued, but these professionals provide foundational work that's vital to many facets of your business. They build and maintain the infrastructure that collects, cleans, and organizes data. Without data engineers, investing in other technologies like machine learning or agentic AI may fail due to a lack of quality data.
But how do you know hiring data engineers is truly worth it? As with any other business expenditure, it's a question of Return on Investment or ROI. However, the ROI of data engineering is about more than revenue alone. It's about quantifying efficiency, risk reduction, and how adding data engineers to your team can accelerate your growth.
In this article, we'll help you make the decision to hire data engineers by understanding their true ROI. You'll discover simple Key Performance Indicators (KPIs) and reporting methods you can use to demonstrate the value of a data engineering team.
Measuring a Data Engineer's Value
If you've never worked with data engineers before, it's easy to overlook this critical role. It's also common to get data engineers confused with data scientists and data analysts. Before you hire data engineers, it helps to understand the distinction between these roles:
- Data Engineers build and maintain the systems that ensure data is well-organized and reliable, making it accessible to the rest of your team.
- Data Scientists use advanced analytics, machine learning, and other techniques to build predictive models and help teams automate data-driven decisions.
- Data Analysts interpret data to identify patterns, uncover trends, and create reports that can help guide your business strategy.
In other words, data engineers create the infrastructure for other roles. Because this function is primarily on the back-end of your business, it can be difficult to directly measure how much revenue can be attributed to data engineers. That's why ROI for data engineering should consider KPIs around operations and risk mitigation, rather than direct figures like sales.
Data Engineering KPIs
Data engineering ROI can be grouped into three categories: efficiency, data quality and error reduction, and strategic impact. By looking at these qualitative Key Performance Indicators, you can gain a comprehensive overview of the full value of adding data engineers to your team.
Efficiency Metrics
Data engineers are power efficiency boosters. When you hire data engineers who are the right fit for your needs, you'll dramatically reduce the amount of time your team spends on manual or repetitive tasks. Measure the ROI of your data engineers by looking at metrics such as:
Data Latency Reduction: Data latency is the time it takes for data to move from one system to another, such as a CRM to an analytics dashboard, in a form that's ready for analysis. If your engineers reduce this latency from 24 hours to 12, or even fewer, that's a sign that they're adding value to your company.
Manual Effort Savings: Are your data engineers creating platforms that automate data management? Consider how much time your team would normally spend on manual data tasks, then assess the time saved after implementing a data engineer's automated systems.
Pipeline Development Speed: Track how much time it takes for your team to deploy new data sources. Faster deployment means you can leverage more data sooner, enabling more efficient decisions around strategy and growth.
Data Quality & Error Reduction Metrics
You can't make data-driven decisions with poor quality data. If your data is inconsistent or inaccurate, it poses a risk to your entire business because it may lead you down the wrong strategic path. However, a good data engineer mitigates the risks of poor data quality. Your ROI should account for:
Data Error Rate: This refers to the percentage of data rejected by a pipeline or flagged for inaccuracy. Data engineers will help bring this number as close to zero as possible, reducing erroneous reports.
System Uptime and Reliability: This metric indicates how well data engineers keep your systems maintained. With a high percentage of uptime and minimal server issues, your organization can reduce time wasted on troubleshooting.
Time-To-Recovery (TTR): Even the most robust data systems can experience unexpected downtime due to power outages or other issues. TTR measures how quickly a data engineer can restore functionality. Lower TTRs mean less disruption to your business.
Strategic Impact Metrics
Finally, measuring the ROI of hiring data engineers means assessing their impact on your overall business strategies. While there isn't always a clear one-to-one correlation between a data engineer's outputs and your business outcomes, understanding these KPIs helps you understand the value that you'll receive when you hire data engineers.
Adoption Rate of Data Assets: Monitor how many teams or projects are actively using the data systems built by your data engineers. High adoption proves these data assets are valuable to your organization overall.
Acceleration of Strategic Projects: Use your data engineers to develop a specific project, such as an agentic AI system or a new analytics dashboard, then document how quickly they can implement this. Fast delivery of a successful project means they're adding value to your team.
Scale Your Data Engineering Team with The JADA Squad
Now that you understand how to measure the ROI of a data engineer, you know the value that these professionals can bring to your organization. But the demand for specialized data roles is at an all-time high, which means traditional hiring can be slow and expensive. If you need to hire data engineers, consider staff augmentation from The JADA Squad.
JADA offers a smarter, faster alternative for onboarding specialized data and AI talent. We offer teams of world-class data engineers and other professionals who can seamlessly integrate within your in-house team and help you scale.
Contact JADA today and see how our expert data talent can transform your business.
Frequently Asked Questions
Is AI replacing data engineers?
No. Data engineers are still essential for building, monitoring, and optimizing data systems. In fact, AI requires robust infrastructures developed and maintained by data engineers in order to function.
How much does a data engineer pay?
Salaries for data engineers vary based on region, experience level, and specialization. However, companies that are looking to hire data engineers through traditional recruiting need to be prepared to pay significant salaries to remain competitive, as well as training and recruitment fees. That's why staff augmentation is often a more affordable model.
Is a data engineer in demand?
Yes. Data engineers are currently a very high-demand role. As companies rely more and more on data and AI, they need more professionals who can build and maintain reliable data architectures.
What is the difference between a data engineer and a data scientist?
Data engineers build the systems and pipelines that ensure data is accurate, accessible, and ready to use. Data scientists, on the other hand, use this data to develop predictive models and strategic business insights. Although these roles often overlap, they require distinct skillsets.
