Posted 5 days by Jonny Grange
Hiring your first data professional is rarely as simple as choosing a job title. Most businesses reach this point when something is not quite working. Reporting takes too long, teams question the numbers, or decisions are being made without clear insight. At that stage, the need to hire becomes obvious. What is less clear is who to hire.
Data roles often get grouped together, but they solve very different problems. Hiring a Data Analyst, BI Developer or Data Engineer will lead to very different outcomes depending on how your data currently works.
In this guide, we explain how to approach that decision. The focus is on helping you identify what your business actually needs, so your first hire delivers impact rather than adding complexity.
If you want a broader view of hiring data talent, including how to assess and structure your process, our data recruitment guide covers the full picture.
Why this decision is often harder than expected
Many businesses assume they need “someone in data” without being clear on what that means in practice. The challenge is that data roles are not always clearly defined. Titles vary between companies, responsibilities overlap, and similar tools are used across different positions.
This often leads to hiring based on familiarity rather than need. A business may ask for a Data Scientist because it sounds advanced, when the real issue is inconsistent reporting. Another may look for an analyst, when the underlying problem sits in how data is structured.
We see this regularly. The hire itself is often capable, but the role does not match the problem. That is where progress slows and expectations become difficult to manage.
Taking time to define what is actually missing avoids this. It leads to a clearer brief and a hire that can contribute from the start.
Start with the problem, not the job title
Before deciding on a role, it helps to look at how data is currently being used across your business.
In most cases, the signals are already there. Reports may take too long to produce, different teams may work from different numbers, or data may need to be manually pulled together from several systems. These are all common issues, but they point to different types of hires.
Focusing on the problem first makes the decision far more straightforward. It shifts the conversation from job titles to outcomes.
What different problems usually indicate
If your teams struggle to access clear reports or dashboards, the issue is usually visibility. This often points towards hiring a Data Analyst who can bring structure to reporting and define consistent metrics.
If reporting exists but lacks consistency, the challenge is often how data is organised. Different teams may use different definitions or rely on separate dashboards, which reduces trust in the numbers. In this case, a BI professional is often a better fit.
If data is difficult to access or requires significant manual effort to maintain, the problem is usually structural. This is where a Data Engineer can have the biggest impact by improving pipelines and data flow.
When reporting is already strong and the focus shifts towards forecasting or optimisation, more advanced roles such as Data Scientists become relevant.
Why this approach leads to better hiring decisions
Hiring based on job titles alone often leads to misalignment. The role may look right on paper, but it does not address the immediate challenge the business is facing.
We often see businesses hire ahead of their needs. For example, bringing in a Data Scientist before data is clean or accessible. While the individual may be strong, their ability to deliver is limited by the environment.
Focusing on the problem avoids this. It allows you to prioritise the hire that will create the most impact now, while setting a clear direction for future growth.
The most common first data hires
Once you are clear on the problem, it becomes easier to map that need to a specific role.
Most first data hires fall into one of four categories. Each supports a different stage of data maturity and addresses a different type of challenge.
Data analyst
A Data Analyst is usually the first data hire when the issue is visibility.
In many growing businesses, reporting exists but it is inconsistent or difficult to maintain. Teams often rely on spreadsheets or manual processes to understand performance, which slows decision-making and creates uncertainty.
A Data Analyst brings structure to this. They define key metrics, build dashboards and provide clear insight that teams can rely on. Instead of reacting to fragmented data, the business starts working from a consistent view of performance.
We see this as the most common starting point. Improving visibility tends to unlock faster decisions and creates a stronger foundation for future hires.
BI analyst or BI developer
A BI hire becomes more relevant when reporting already exists but cannot be trusted.
This often happens when different teams use different data sources or definitions. Over time, this creates multiple versions of the truth, making it difficult to rely on reporting when making decisions.
A BI professional focuses on standardising this. They build structured data models, align reporting across teams and ensure consistency in how metrics are defined and used.
This role is less about creating new reports and more about making existing data reliable. For businesses dealing with fragmented reporting, this is often the most effective next step.
Data engineer
A Data Engineer is typically the right first hire when the main issues sit behind the scenes.
If data is spread across systems, pipelines are unreliable or reporting requires ongoing manual work, the problem is usually structural rather than analytical.
This role focuses on building and maintaining the data infrastructure. It improves how data is collected, stored and accessed, which allows reporting and analysis to scale more effectively.
The impact may not be immediately visible to every stakeholder, but it removes bottlenecks and supports long-term growth. Without this foundation, reporting and analytics often struggle to develop.
Data scientist
A Data Scientist is usually not the first hire unless strong foundations are already in place.
This role focuses on modelling, forecasting and more advanced analysis. It is most effective when data is clean, accessible and already used consistently across the business.
If those conditions are not in place, the role can become difficult to deliver. We often see businesses hire for data science too early, which leads to underutilised skills and unclear expectations.
When the groundwork is in place, however, this role can add significant value by supporting more advanced decision-making and optimisation.
Once you look at the roles in this way, the decision usually becomes much clearer. It comes down to identifying which of these problems is most relevant to your business today.
Signs you may be hiring the wrong role
Even with a clear plan, it is still possible to misalign the role if expectations are not well defined.
One common sign is when the role becomes too broad. If you are trying to hire someone to manage reporting, build pipelines, define strategy and deliver advanced modelling, the scope is likely too wide for a first hire.
Another signal is when the brief changes during the hiring process. This often indicates that the underlying problem has not been fully defined, which makes it difficult to assess candidates consistently.
You may also find that reporting remains inconsistent or stakeholders still lack confidence in the data after hiring. This usually points to a mismatch between the role and the problem it was meant to solve.
Recognising these signs early helps you adjust your approach and avoid committing to the wrong hire.
Why your first data hire should stay focused
Your first data hire plays a key role in shaping how your business uses data going forward. For that reason, clarity and focus are more important than trying to cover every possible need.
In most cases, the most effective first hire is the one that delivers visible impact quickly. This is often a role that improves reporting, increases confidence in the data and supports better decision-making.
Trying to solve multiple problems at once can slow progress and make it harder to define success. A focused role creates clearer expectations and allows the business to see value early.
Once that foundation is in place, it becomes much easier to decide what comes next, whether that is improving infrastructure, expanding BI capability or introducing more advanced analysis.
How we support employers hiring data talent
Hiring your first data professional often comes down to defining the role correctly. Without that clarity, even a well-structured hiring process can struggle to deliver the right outcome.
We work with hiring managers, Heads of Data and internal talent teams to understand where the gap sits. This includes reviewing how data is currently used, where reporting slows down and what is limiting progress.
From there, we help shape the role around the problem rather than the title. This leads to clearer briefs, stronger candidate alignment and more effective hires.
We also provide insight on market expectations, salary benchmarks and candidate availability, helping you move through the process with confidence.
Choosing your first data hire is less about selecting the right title and more about understanding what your business needs most right now.
For many teams, that starts with improving visibility and building trust in reporting. For others, it means addressing structural issues that limit how data is used.
Taking the time to define this clearly leads to better hiring decisions and reduces the risk of delays or re-hiring later on.
If you are planning your first data hire or reviewing your approach, our ultimate guide to data recruitment provides a more detailed view of how to structure your hiring process and build a data team that supports long-term growth.
