Data Engineer, Data Analyst, Data Scientist: Who Should You Hire First?

The question comes up regularly in discussions with executives launching their data strategy: which profile should you hire first? A data scientist for predictive models? A data analyst for operations? A data engineer to build the foundation? The answer is never universal, but it follows an inexorable logic: that of your organization's data maturity and immediate needs.

Many companies jump in by hiring a data scientist first, lured by the promise of artificial intelligence and sophisticated algorithms. A few months later, that same data scientist spends most of their time cleaning Excel files and hacking together scripts to extract data scattered across systems. A waste of talent that reveals a fundamental startup mistake.

Building a high-performing data team is first about understanding what each of these roles concretely brings to the table, then orchestrating hiring in an order that creates value from the first months onward. An approach that aligns with broader thinking on recruiting and retaining data talent.

The three roles and their real missions

Before talking about priorities, let's clarify what these three profiles actually do on a day-to-day basis. Job titles are often misunderstood, leading to poor hiring decisions.

The data engineer is the infrastructure builder. They construct the pipelines that collect, transform, and deliver data. They ensure data arrives in the right place, in the right format, reliably and automatically. Without their work, there's no solid foundation. This involves database modeling, workflow orchestration, data quality management at scale. A data engineer thinks in terms of volume, performance, and maintainability.

The data analyst leverages data to answer concrete business questions. They transform numbers into actionable insights for operational teams. Their day-to-day: building dashboards, analyzing performance, spotting anomalies, recommending actions. They master SQL, Excel, visualization tools like Tableau or Power BI. They're the translator between raw data and business decisions.

The data scientist goes further in analytical sophistication. They develop predictive models, test complex hypotheses, explore machine learning techniques to solve problems that can't be handled by standard SQL queries. But here's the catch: to be effective, they need clean, well-structured, accessible data. Without it, they spend time on data preprocessing instead of doing actual science.

The first data hire: laying foundations with a data engineer

For most organizations starting their data journey, the first hire should be a data engineer. The reason is straightforward: without infrastructure, there's no usable data. Without usable data, there's no meaningful analysis or modeling.

We regularly see this scenario play out: a company hires a brilliant data scientist, full of ideas. But data sits scattered across different systems, exports happen manually, quality is uncertain. The data scientist ends up spending 80% of their time on engineering tasks they're neither trained nor motivated for. Within a few months, frustration builds and the talent leaves.

A data engineer starts by mapping what exists: where the data is, how to access it, what its quality looks like. Then they set up the first automated pipelines, often using tools like Airflow, dbt, or cloud-native solutions. They create a data warehouse or data lake based on needs. Within months, the company has a solid foundation: centralized, documented, reliable data.

This foundation changes everything. It allows a data analyst to be productive from day one, without wasting time writing extraction scripts. It allows a future data scientist to focus on what they do best: modeling and predicting.

There are exceptions, though. If your company already has a well-structured information system with clean, accessible databases, and the immediate need is to answer specific business questions, then starting with a data analyst can make sense. This is particularly true in SMEs where transactional data is already well-organized and the urgency lies in operational management.

The second hire: creating business value with a data analyst

Once the foundations are in place, the next hire depends on your strategy and company's priority needs. In most cases, the data analyst comes second.

Why? Because they transform infrastructure into tangible business value. Data is now centralized and accessible. It's time to leverage it to inform daily decisions: tracking KPIs, understanding customer behavior, optimizing processes, identifying growth levers.

The data analyst builds the first dashboards that quickly become indispensable to sales, marketing, and finance teams. They answer ad hoc questions, dig into anomalies, offer recommendations. This constant iteration with business teams builds a data culture across the organization. Teams start developing habits: consulting dashboards, asking data-driven questions, challenging intuitions with numbers. An approach that avoids the cosmetic reporting trap.

This phase is crucial. It justifies the data investment to leadership and teams. It delivers quick wins that build confidence. And it progressively identifies use cases where more advanced approaches, like machine learning, could add extra value.

This is when companies start distinguishing descriptive analytics (what happened) from predictive analytics (what will happen). The data analyst excels at the former. But when questions become more complex, when you need to anticipate behavior, optimize strategies in real time, or handle massive volumes with sophisticated techniques, the need for a data scientist emerges naturally.

The third hire: scaling up and predicting with a data scientist

The data scientist typically comes third, once the organization has reached a certain data maturity. At this stage, data is reliable, descriptive analytics are in place, and the company is ready to move toward more advanced use cases.

The data scientist tackles problems where standard business rules no longer suffice. Predicting customer churn, optimizing marketing campaigns in real time, detecting fraud, personalizing user experience at scale, automating complex decisions. These are projects requiring expertise in statistics, machine learning, sometimes deep learning.

But be careful: hiring a data scientist without first structuring infrastructure and building a data culture risks a costly failure. The data scientist can't do it all alone. They need data that's already available, clean, documented. They need business teams that understand the analytical approach and can frame exploitable problems. They need a data engineer to put their models into production.

This is why order matters. When the data scientist arrives after the data engineer and analyst, they inherit prepared ground. Infrastructure is in place, business teams are comfortable working with data, use cases are identified. They can then focus on what truly creates value: developing and refining models that exceed the capabilities of standard analysis.

In some very specific sectors, particularly research or AI-heavy industries, the order might differ. But for most companies building their data strategy, this sequence remains the most robust.

Beyond order: building a high-performing data team dynamic

Hiring in the right sequence isn't enough. You also need to think about collaboration and complementarity. A high-performing data team isn't a collection of brilliant profiles working in silos. It's a system where each person contributes their piece and skills naturally reinforce each other.

The data engineer builds and maintains pipelines. The data analyst uses them to produce insights. The data scientist leverages those same pipelines to train models. In return, the data scientist can flag infrastructure evolution needs, the analyst can surface data quality issues, the engineer can automate repetitive data prep tasks.

This dynamic requires creating team rituals: regular technical reviews, syncs on business priorities, knowledge-sharing sessions. It also means clarifying responsibilities without creating watertight compartments. A good data analyst should understand data engineering fundamentals. A good data scientist should be able to engage with business teams the way an analyst does.

Finally, anticipate evolution. A growing data team will see specializations emerge: analytics engineer, ML engineer, data architect. But these roles only make sense when the foundation is already solid and volume or complexity justifies splitting responsibilities. Mistakes you can avoid by learning from the experience of analytics leaders.

Conclusion: a data hiring strategy, not an org chart

Deciding who to hire first in a data team isn't a matter of fashion or prestige. It's a strategic decision that must ground itself in your organization's reality: its data maturity, business needs, medium-term ambitions.

For most companies, starting with a data engineer establishes solid foundations. Following with a data analyst quickly creates business value. Adding a data scientist third opens the door to advanced use cases, once infrastructure and culture are ready.

But beyond sequence, team dynamic is what makes the difference. Hire complementary profiles, foster collaboration, clarify roles while maintaining flexibility. Building a high-performing data team means orchestrating talent around a shared vision, not simply filling an org chart.