6 essential data roles for growing teams

As organisations grow, data quickly becomes harder to manage. Reporting requests increase, teams want clearer insight, and relying on one generalist often creates bottlenecks rather than progress.

Growing data teams need structure. Different data roles solve different problems, and hiring in the wrong order can slow delivery and increase cost. In this blog, we outline the six essential data roles most growing teams need, what each role delivers, and when it usually makes sense to hire them.

If you're new to hiring data talent or want to get the full picture first, our data recruitment guide is a good place to start.

Why growing teams need a clear data hiring structure

As organisations grow, data quickly becomes central to decision making. Reporting requests increase, stakeholders want faster answers, and expectations rise across leadership teams. Without a clear hiring structure, data teams often struggle to keep up, even when good people are in place.

From our experience supporting employers with data recruitment, most problems do not come from a lack of effort. They come from unclear role design and hiring without a plan. A structured approach helps you hire the right capability at the right time, avoid overlap, and build a data team that can scale with the business.

Data demand grows faster than internal capability

In growing teams, demand for data insight usually increases before the capability exists to support it properly. Leaders want clearer reporting, teams want answers quickly, and data becomes part of everyday decision making. This often places pressure on one or two people who are expected to cover analytics, reporting and data quality at the same time.

When this happens, delivery slows and confidence in the data drops. Hiring with a clear structure allows you to spread responsibility across defined roles, so insight, reporting and data reliability can all improve together rather than competing for attention.

Hiring the wrong role first slows delivery

One of the most common mistakes we see in data hiring is bringing in the wrong role too early. Employers often hire advanced roles such as data scientists or AI specialists before basic reporting, pipelines or definitions are in place.

Without strong foundations, even experienced hires struggle to deliver value. A clear hiring structure helps you prioritise roles that support current needs, reduce rework, and avoid paying for skills your team is not ready to use yet.

Data maturity determines which roles add value

Not every data role adds value at every stage of growth. Early-stage teams benefit most from visibility and insight. Scaling teams need reliability and trusted metrics. More mature teams focus on optimisation, automation and governance.

Understanding your current level of data maturity helps you decide which roles will have the biggest impact now, not just which titles sound impressive. This approach aligns closely with the data roadmap and hiring principles we cover in our ultimate guide to data recruitment, and it helps employers build data teams with purpose rather than guesswork.

The 6 essential data roles for growing teams

As your organisation grows, data roles start to separate into clear specialisms. Each role solves a different problem and supports a different stage of growth. Most teams do not need all six roles at once, but they do need to understand which hires unlock progress and which ones can wait.

Based on what we see across data recruitment, the roles below form the core of a well-structured data team. Hiring them in the right order helps you improve insight, trust and delivery without adding unnecessary cost or complexity.

1. Data analyst

For many growing teams, the data analyst is the first role that creates visible impact. This hire focuses on turning existing data into insight that supports day-to-day decisions across the business. When reporting is inconsistent or stakeholders rely on spreadsheets, a strong analyst brings clarity quickly.

Data analysts sit close to commercial and operational teams. They help leaders understand performance, answer ad-hoc questions and highlight trends that would otherwise be missed. In early-stage data teams, this role often becomes the bridge between raw data and business action.

What this role focuses on day to day:

• Building and maintaining reports and dashboards

• Answering stakeholder questions with clear analysis

• Defining and tracking KPIs and performance metrics

• Supporting decision making with insight rather than opinion

Core skills employers should look for:

• Strong SQL for querying and analysis

• Experience with reporting or BI tools

• Clear communication with non-technical stakeholders

• Confidence working with imperfect or incomplete data

When growing teams typically hire this role:

Most teams hire a data analyst when reporting requests increase and leaders need consistent answers. This is often the first dedicated data hire once basic data collection is in place.

2. BI developer or analytics engineer

As reporting grows, consistency becomes the challenge. Different teams often report the same numbers in different ways, which leads to confusion and loss of trust. This is where a BI developer or analytics engineer becomes essential.

This role focuses on shared definitions, trusted dashboards and reliable reporting layers. They reduce manual reporting and ensure that everyone works from the same version of the data. In growing teams, this hire often stabilises reporting and frees analysts to focus on insight rather than maintenance.

What this role focuses on day to day:

• Building and maintaining reporting models

• Creating shared metrics and definitions

• Improving dashboard performance and reliability

• Supporting self-serve reporting across teams

Core skills employers should look for:

• Strong SQL and data modelling experience

• Experience with BI platforms such as Power BI, Tableau or Looker

• Understanding of reporting layers and semantic models

• Ability to balance technical detail with user needs

When growing teams typically hire this role:

Teams usually hire this role when reporting becomes business-critical and inconsistencies start causing friction. It often follows the data analyst hire once demand and complexity increase.

3. Data engineer

As data usage increases, reliability and availability become major risks. Analysts and BI teams depend on clean, timely data. When pipelines break or data refreshes fail, delivery slows across the business. This is where a data engineer becomes essential.

Data engineers focus on the foundations that support the entire data function. They build and maintain pipelines, manage data flows and ensure that data is available when teams need it. Without this role, analytics teams often spend too much time fixing issues instead of delivering value.

What this role focuses on day to day:

• Building and maintaining data pipelines

• Ingesting data from internal and external sources

• Monitoring data quality and reliability

• Supporting scalable data platforms

Core skills employers should look for:

• SQL and Python for data processing

• Experience with data warehouses or lakehouse platforms

• Pipeline orchestration and monitoring experience

• Understanding of data reliability and testing

When growing teams typically hire this role:

This role is usually hired when data volumes increase and manual processes no longer scale. It often becomes a priority once analytics and BI teams depend heavily on timely, reliable data to operate.

4. Data scientist

Data scientists are often seen as the most visible data role, but they add the most value once strong foundations are in place. In growing teams, this role focuses on deeper analysis, modelling and experimentation that goes beyond standard reporting.

When hired at the right time, data scientists help you move from describing what has happened to understanding why it happened and what is likely to happen next. When hired too early, they often spend time cleaning data or rebuilding reports, which limits their impact and slows progress.

What this role focuses on day to day:

• Analysing complex data sets to uncover patterns and drivers

• Building statistical or predictive models

• Supporting experimentation, forecasting and scenario analysis

• Presenting findings clearly to senior stakeholders

Core skills employers should look for:

• Strong Python or R for analysis and modelling

• Solid understanding of statistics and data science methods

• Experience working with business problems, not just models

• Ability to explain technical findings in plain language

When growing teams typically hire this role:

Teams usually hire a data scientist once reporting is stable and data quality is trusted. This role delivers the most value when analysts, BI and engineering foundations already exist.

5. Machine learning engineer or AI engineer

Machine learning and AI engineers focus on taking models and turning them into working systems. While data scientists often build models, this role ensures they run reliably in real products and processes.

In growing organisations, this hire becomes important when AI or automation moves beyond proof of concept. Without this role, models often stay in notebooks and never reach production. With the right hire, teams can scale AI use cases safely and consistently.

What this role focuses on day to day:

• Deploying and maintaining machine learning models

• Building pipelines for training, inference and monitoring

• Supporting AI-driven features and automation

• Ensuring models perform reliably over time

Core skills employers should look for:

• Strong Python and engineering experience

• Understanding of model deployment and monitoring

• Experience with cloud platforms and data pipelines

• Knowledge of production ML or applied AI systems

When growing teams typically hire this role:

This role is typically hired once the business commits to running AI or machine learning in production. It usually follows data science or advanced analytics capability.

6. Data architect

As data teams grow, complexity increases. New tools, platforms and pipelines are added, often quickly. Without clear structure, this leads to duplication, rising costs and long-term data issues. The data architect brings order to this complexity.

This role focuses on the overall design of your data environment. They define standards, guide platform decisions and ensure data supports both current and future needs. For growing teams, this role helps prevent expensive rework later.

What this role focuses on day to day:

• Designing and maintaining data architecture

• Defining data models, standards and best practice

• Supporting governance, security and scalability

• Guiding platform and tooling decisions

Core skills employers should look for:

• Strong experience with data platforms and architecture

• Understanding of governance, security and access control

• Ability to balance technical design with business needs

• Experience supporting growing or multi-team environments

When growing teams typically hire this role:

Teams usually hire a data architect when their data environment starts to scale across teams or platforms. This role is often introduced to reduce long-term risk and support sustainable growth.

Building a strong data function is less about chasing job titles and more about hiring the right roles at the right time. For growing teams, clarity around data maturity, role scope and hiring order makes a real difference to delivery speed, cost and long-term impact.

If you are planning to grow your data team or want to sense-check which roles will add value right now, having a clear structure helps you avoid common hiring mistakes and focus on what the business actually needs.

Looking for more detail on hiring data talent? Read our ultimate guide to data recruitment.