How to build a data team: the profiles you need from bi to ai

Building a data team usually starts with a specific need: automating reporting, integrating information from different systems, improving forecasting, or developing an artificial intelligence application. As these projects increase, so do the dependencies between data, infrastructure, analytics, and product.

The appropriate composition depends on that starting point. A company that is still consolidating information manually from CRM and ERP has different priorities than one that has a stable data platform and wants to bring machine learning models into production.

Therefore, planning the team from a predetermined list of positions can lead to incorporating specialization before having the necessary foundation to leverage it. The sequence should respond to the capabilities the business needs and the level of maturity achieved by its data platform.

The goal is to build those capabilities progressively, from business intelligence and data engineering to machine learning and ai, incorporating specialization when there is a real need that justifies it.

The data team must be built around results

Before defining positions, it is advisable to determine what the team should produce over the next few months. This decision helps separate immediate needs from capabilities that will be necessary at a later stage.

If the priority is to have consistent KPIs for management, the effort can initially focus on business intelligence, modeling, and integration. If the goal is to consolidate dozens of sources within a reusable platform, data engineering gains weight. When advanced forecasting, scoring, recommendations, or intelligent automations appear, machine learning capabilities begin to be justified.

This relationship between objectives and capabilities also allows for better sizing of hiring. A technically advanced profile contributes little if they remain blocked because the sources they need are not yet integrated or the data lacks sufficient quality.

The maturity of the data conditions which profile adds the most value

The sequence of incorporation is especially important when a company wants to advance toward ai. Before deploying models or intelligent applications, there must be a foundation capable of supplying reliable, accessible, and governed information.

For example, hiring a machine learning engineer to develop predictive models may have little impact if preparing each dataset requires weeks of manual work. In that scenario, resolving the pipelines and the availability of information first can accelerate both current and future projects.

The following matrix allows relating common needs with capabilities and profiles:

The table represents a possible progression, not a mandatory organizational structure. Some companies will develop several capabilities simultaneously, while others will need specific specialists much earlier due to the nature of their product.

Business intelligence turns data into usable information

Business intelligence is often one of the first visible capabilities of a data team because it directly connects information with business decisions.

The business intelligence specialists work on analytical models, metrics, dashboards, and other forms of consumption that allow finance, marketing, sales, operations, or management to work on a shared reference.

Their function deepens as maturity increases. The work can evolve from building reports to defining semantic models, establishing reusable KPIs, enabling self-service analytics, and ensuring that different areas interpret metrics consistently.

A backlog of dashboards can hide a structural problem

When all business questions require a new report, the BI team can quickly become a bottleneck. Increasing the number of analysts may temporarily relieve the load, but it is advisable to investigate what is generating that dependency.

Non-standardized metrics, difficult-to-query sources, or insufficient analytical models can force repeated resolutions of similar problems.

A well-designed semantic layer and reusable datasets allow BI to stop working exclusively on demand and build capabilities that different teams can use. This evolution also prepares the organization for analytical interfaces based on AI, where the meaning of each metric must be correctly defined before allowing queries through natural language.

Data engineering builds the foundation that allows scaling

As the number of sources and use cases increases, integrating data ceases to be a secondary task. Pipelines need to run reliably, transformations must be maintainable, and consumers need to know when information will be available.

The data engineers work on this layer. Their responsibility may include ingestion, transformation, modeling, orchestration, and observability of the flows that connect operational systems with analytical platforms and applications.

This capability allows moving from integrations built for a single report to datasets that can be reused in BI, analytics, machine learning, and digital products.

Database specialization appears with complexity

Not all organizations need a dedicated database specialist from the beginning. The need increases when performance, availability, scalability, replication, security, or recovery start to require specialized attention.

The database specialists can intervene in these scenarios, both on relational systems and on other storage technologies used by the architecture.

The decision should respond to the criticality of the platform. A managed database with a stable load may require little specialized intervention, while a high-availability application with large volumes and demanding performance requirements may justify that capability much earlier.

Machine learning changes the team's needs

A mature data platform opens the possibility of advancing from descriptive analysis to systems capable of generating predictions, classifications, or recommendations.

At this stage, responsibilities different from those of business intelligence emerge. The team needs to prepare datasets for training, develop models, evaluate results, and subsequently integrate those models with applications or processes.

The machine learning engineers work especially on turning models into systems that can be used under real conditions. This involves connecting machine learning development with software, data, and infrastructure.

The availability of historical data and its quality condition how far this capability can advance. If each experiment needs to manually reconstruct the dataset, a considerable part of the team's time will continue to be dedicated to resolving preparation issues.

Bringing a model into production introduces an operational stage

Achieving good results during an experiment is only part of the cycle. Once deployed, the model needs monitoring, versioning, infrastructure, update mechanisms, and controls that allow for detecting degradations.

Here, MLOps practices and a closer relationship with devops engineers capabilities emerge.

The need for this specialization depends on the number of models, frequency of deployment, and criticality. A company with a single model updated a few times a year can manage the operation with a simple structure. An organization with multiple models integrated into critical processes needs a much more systematic capability.

Enterprise AI expands the team beyond the model

Current applications of artificial intelligence can incorporate generative models, RAG, agents, semantic search, external tools, and enterprise systems. This expands the dependencies around the data team.

An internal assistant based on RAG, for example, needs to access knowledge sources, retrieve relevant information, respect permissions, and provide context to the model. The work involves data, but also software integration, security, evaluation, and architecture.

The AI capability can be integrated over an existing data platform and work alongside engineering to bring these use cases into real applications.

The incorporation of AI does not imply that BI or data engineering lose relevance. As applications use more information automatically, the importance of having reliable sources, ownership, and controls over how data is consumed increases.

What to define before expanding the team

Before opening new positions, it is advisable to relate hiring to the results expected. This review helps distinguish a capacity limitation from an architecture, process, or priority problem.

The main elements to define are:

Priority use cases: what the team needs to deliver over the next quarters.

Available sources: what information exists and what work is required to prepare it.

Current architecture: what capabilities the platform already provides and where there are limitations.

Ownership: who is responsible for data, metrics, and critical components.

Available skills: what knowledge already exists and what represents a gap.

Dependencies: what initiatives compete for the same capabilities.

Need horizon: whether the specialization will be permanent or concentrated in a specific stage.

With this analysis, it becomes easier to decide whether the company needs to hire permanently, develop capabilities within the team, or bring in specialists to accelerate a specific phase.

Centralized, embedded, or hybrid: how to organize the data team

The selection of profiles resolves part of the structure. It is also necessary to decide where they will work and how they will relate to the areas that consume their results.

A centralized model concentrates professionals and platform within the same function. It facilitates standards, reuse, and governance, especially during the early stages, although it can create distance with the specific needs of each area when demand increases.

Embedded teams bring data closer to the business context

In an embedded model, certain professionals work directly with product, marketing, finance, or operations. This reduces the distance between the need and the person performing the analysis, facilitating specialization in a domain.

The risk arises when each area starts to build its own metrics, pipelines, or tools without sufficient coordination. Over time, duplications and incompatible definitions may appear.

Therefore, the embedded model often requires shared standards even if the professionals are distributed.

The hybrid model separates platform and domain specialization

A hybrid structure can maintain central capabilities responsible for platform, governance, quality, and standards while certain profiles work close to business units.

This configuration allows for reusing infrastructure and common definitions without forcing all requests to go through a single central team.

Its operation depends on establishing ownership precisely. Responsibilities for platform, datasets, metrics, and use cases must be sufficiently clear to avoid problems being spread across several teams without an effective responsible party.

How to evolve the team from bi to ai

The construction of the team can follow a sequence related to the maturity of the data.

An initial stage usually focuses on achieving reliable reporting. Teams need to reduce manual processes, consolidate KPIs, and establish a common reference for the most important metrics.

The next priority may be to develop a data platform capable of integrating sources and producing reusable datasets. Data engineering then gains importance because the goal shifts from resolving individual reports to building infrastructure that supports multiple consumers.

When that foundation works, the company can expand advanced analytics through forecasting, segmentation, and other models. Machine learning appears when those models need greater sophistication or must be integrated into products and processes.

Subsequently, enterprise AI can use structured and unstructured data to build assistants, RAG systems, automations, or agents. If these solutions scale, more demanding needs for operation, evaluation, observability, and governance arise.

Signs that the team needs more capacity or specialization

The need to expand a team does not always manifest as a direct lack of personnel. It can appear through dependencies and delays that repeatedly affect different initiatives.

Some relevant signs are:

The BI backlog increases and areas wait too long to obtain information;

The same data engineers are a dependency for practically all projects;

Critical pipelines present recurring incidents or require too much manual maintenance;

Machine learning specialists spend a significant part of their time preparing data;

There are models with good experimental results that take too long to reach production;

One person concentrates critical knowledge about certain sources, metrics, or platforms;

New AI projects are delayed because the data architecture still cannot provide reliable and governed information.

These signs should be analyzed within the context of the platform. Adding another person can increase capacity when there is a real backlog, but it will have a limited effect if the bottleneck comes from manual processes or architectural decisions that continue generating repetitive work.

A data team must grow with the capacity the business needs

The evolution from business intelligence to artificial intelligence requires different capabilities, but it does not force building an organization with all specialized profiles from day one.

The initial decisions should respond to the results the company needs and the dependencies that prevent achieving them. BI can establish metrics and analytical consumption; data engineering creates a reusable foundation; database specialists appear when the platform gains criticality; machine learning expands predictive possibilities; and AI applications incorporate new integration and operation needs.

As maturity increases, these capabilities begin to work together. An AI system may depend on pipelines maintained by data engineering, information governed by the platform, and components deployed through shared practices with engineering.

The structure of the team must reflect those dependencies and evolve when use cases change. In this way, each new addition responds to a capability that the organization needs to develop, operate, or scale, keeping the investment in talent aligned with the actual evolution of its data strategy.