Given the increasing demands of data-driven organizations, the value of a strong data team is impossible to ignore. These teams don’t just manage data—they also build scalable systems that modern businesses rely on, keep data safe and accessible, and unlock insights that can become invaluable competitive advantages.

Because of this, understanding the roles that comprise these teams, the different forms they can take, and the factors that set high-performing teams apart is essential—not only for getting new data teams off the ground but also for keeping existing teams healthy, happy, and motivated to deliver.
The most common roles within a data team
While businesses can vary widely, the average data-driven organization’s needs—data quality management, consistent governance, security, and the ability to scale data environments as necessary—are fairly consistent. Therefore, the following roles are common to data teams across industries:
Chief data officer (CDO)
The CDO defines the organization’s overarching data strategy, aligns it with business goals, and ensures that data initiatives drive value across the company.
Daily focus: The CDO sets data strategy, coordinates cross-functional data initiatives, and advocates for data-driven decision-making at the executive level while overseeing the data team’s performance.
Needs from leadership: Being a leader doesn’t mean a CDO can go it alone. To excel, each needs a direct line to C-suite executives, the authority to drive organizational change, and the key resources they need to implement large-scale data initiatives.
Data engineers
Data engineers build and maintain organizational data infrastructures. In addition to ensuring efficient data flow and storage, they also create robust pipelines and systems that form the foundation of data operations.
Daily focus: These engineers design and optimize ETL processes, maintain data warehouses and lakes, troubleshoot pipeline issues, and collaborate with data consumers and cross-functional teams (like operations and finance) to understand their data needs and use cases.
Needs from leadership: This role requires clear project priorities from data leaders and stakeholders, access to resources for continuous learning about new technologies, and support for implementing best practices in data architecture.
Data analysts
Data analysts and analytics teams clean, organize, and analyze data to uncover actionable insights. They also bridge the gap between raw data and business decisions by creating reports and visualizations.
Daily focus: These analysts clean and prepare datasets, perform statistical analyses, create dashboards and reports, and present their findings to stakeholders.
Needs from leadership: They need leadership to provide access to relevant data sources, articulate clear business objectives to guide their analyses, and create opportunities for skill development in statistics and data visualization.
Data scientists
Data scientists develop advanced analytical models using machine learning (ML) and other statistical techniques to solve complex problems and, ultimately, drive innovation.
Daily focus: These scientists develop and test predictive models, conduct exploratory data analysis, collaborate with domain experts, and translate complex findings into easily digestible, actionable insights.
Needs from leadership: More than anything, data scientists need the autonomy to explore innovative approaches, access to high-performance computing resources, and support for publishing and presenting findings externally.
Business intelligence (BI) specialists
BI specialists design and implement platforms that make data accessible and understandable for decision-makers across the organization.
Daily focus: These specialists work with stakeholders to understand reporting needs, build and maintain internal dashboards, optimize data visualization techniques based on key metrics, and ensure the accuracy of reports.
Needs from leadership: Clear communication channels with various departments, support for BI tool selection and implementation, and resources for enhancing data visualization skills are what these individuals need most.
Data governance specialists
Data governance specialists ensure compliance with regulations and define policies for secure and ethical data usage across the organization.
Daily focus: These specialists develop and enforce data governance policies, conduct data audits, manage data access controls, and keep teams well-versed regarding data handling best practices.
Needs from leadership: They need executive support for implementing governance policies, resources for staying up-to-date on regulatory changes, and the authority to enforce data standards across departments.
Data architects
Data architects design the blueprint for an organization’s data management systems and ensure scalability, efficiency, and alignment with business needs.
Daily focus: These architects design data models, define data integration strategies, collaborate with IT on infrastructure decisions, and optimize data flows across systems.
Needs from leadership: What they need from leadership is involvement in high-level strategic planning, support for long-term architecture initiatives, and resources for evaluating emerging data technologies.
ML engineers
ML engineers build and deploy ML models to bridge the gap between data science experimentation and real-world applications.
Daily focus: These engineers build scalable ML systems, optimize model performance, work with data scientists to implement models, and monitor those models after deployment.
Needs from leadership: They need support for experimentation with new ML technologies, resources for continuous learning, and clear alignment between ML projects and business objectives.
Typical data team structures
There’s no one way of structuring a team that is, in itself, a hallmark of high performance. In reality, data teams can and do adopt several different structures, each with its own advantages and disadvantages for enabling high performance.
Overall, there are three main structural approaches that teams do take: centralized, decentralized, and hybrid models.
Centralized team structures
Centralization means that all data resources and personnel are part of one central team that serves the entire organization—the hub of a wheel, if you will.
Pros:
- Consistent data governance and standards
- Efficient resource allocation and reduced redundancy
- Enhanced knowledge sharing and mentorship opportunities
- Greater alignment of data resources with company-wide priorities
Cons:
- The potential for slower response times to individual departments’ needs
- Risk of being out of touch with specific business unit requirements
- Possible bottlenecks in decision-making and project execution
Decentralized team structures
By comparison, a decentralized model embeds data professionals within individual departments or business units to serve their needs more closely.
Pros:
- Faster response to department-specific needs
- Deeper understanding of domain-specific challenges
- Optimal agility and flexibility for addressing localized priorities
- Tailored solutions for each business unit
Cons:
- Contribution to inconsistent data practices across the organization
- Potential duplication of efforts and resources
- Challenges in knowledge sharing between teams
- Risk of creating data silos
Hybrid team structures
A hybrid model combines elements of both centralized and decentralized approaches, depending on the organization’s needs.
Pros:
- Balance of consistency with flexibility
- Mixture of company-wide standards and department-specific expertise
- Collaboration between central and embedded teams
- Scalability for growing organizations
Cons:
- Greater communication overhead to avoid conflicts
- Increased management complexity
- Confusion due to fluid roles and responsibilities
Factors that affect overall data team performance
The best structure for high-performing data teams depends on the following factors:
- Organization size: Larger companies often benefit from hybrid models since they enable leaders to balance centralized oversight with decentralized execution (a best of both worlds scenario). Smaller organizations, however, may prefer centralized structures because they’re typically much more efficient and resource-friendly.
- Business goals: The structure that data teams take should, as much as possible, directly support the organization’s overall objectives and data strategy. When business goals require streamlined governance and efficient resource allocations, a centralized team approach may be the natural choice. Alternatively, for a business model that involves rapid innovation, decentralized teams could be more advantageous.
- Industry requirements: For highly regulated industries like healthcare and finance, either a centralized or hybrid team structure might be preferable. Centralized team structures can be ideal when the regulatory landscape is highly complex, as it supports uniform data handling and easier auditing processes. However, the flexibility that’s inherent in a hybrid team approach could prove more beneficial if the regulatory landscape itself is constantly in flux.
- Data maturity: Organizations that begin with a centralized data team may shift to decentralized or hybrid models as they mature. By doing so, data leaders can enable key team members to specialize and work more autonomously with different business units while a core team remains dedicated to overarching strategy and governance needs.
6 key characteristics of a high-performing data team
Not all data teams operate at the same level. While any functional data team can support a business’s needs, the best teams consistently deliver reliable insights, adapt quickly to change, and align their work with organizational goals.
So what sets these high-performing teams apart?
Six key characteristics consistently emerge from industry observations and best practices as essential for sustained success:
- Smaller, more agile teams and multidisciplinary skill sets
First, and perhaps foremost, high-performing data teams tend to run lean—they typically consist of fewer than 10 members. However, the members of these groups tend to own and employ the multidisciplinary skill sets they need to collectively discover, build, and maintain data products throughout their lifecycle.
Teams that run small in this way can typically communicate and collaborate more effectively since fewer members requires less communication overhead, as a rule. This also means that when projects and priorities shift, as both tend to do in data environments, less communication overhead also allows teams to pivot faster and adjust to new tech and tools as necessary while iterating faster on solutions together.
- Strong collaboration and communication
In addition to a smaller team size, data teams that consistently perform above the average tend to prioritize clear communication, including the ability to translate complex data insights into accessible narratives for less technical audiences. This proves to be a critical advantage since it translates into an ability to more effectively engage with key stakeholders in the organization.
As a result, these teams’ work increasingly delivers meaningful value—be this through improved decision-making or measurable business outcomes.
As a secondary but no less valuable benefit of these communication skills, high-performing data teams also contribute to improving data literacy throughout the organization, which empowers others to engage with data more effectively. On the whole, this improves other teams’ ability to interpret insights more effectively and allows them to make more of their own strategic contributions to the organization.
- A dedicated product mindset
High-performing data teams also tend to operate with a shared product mindset, focusing on delivering data products that will provide measurable value to the business. This typically involves proactive engagement with key stakeholders, which results in high-performing teams that are in a better position to identify potential opportunities for transformation (as opposed to passively responding to requests).
When high-performing data teams hold product mindsets, they emphasize balancing the total cost of ownership against the expected value. This enables them to “fail fast forward” during project discovery phases.
- A shared obsession with data excellence
Despite their comparative willingness to iterate, experiment, and make mistakes, there is one thing that these data teams aren’t willing to compromise on: their unwavering focus on data quality and data excellence.
As such, these teams will be more apt to adopt mature engineering practices—such as automation, robust testing, and programmatic governance—to ensure reliability and scalability. They will also seek to strike and maintain a balance between innovation and structured consistency, and more often than not, they’ll work to ensure that their results are repeatable and sustainable over time instead of contenting themselves with one-off successes.
- A data-driven decision-making culture
These data teams also leverage data consistently as part of their decision-making processes. By employing data and evidence, tight-knit data professionals eliminate biases and emotions from their decision-making processes. Embracing objectivity in this way then helps them foster fairness, promote trust, and prioritize transparency in their internal cultures.
In addition to making more effective decisions, this approach also underscores these teams’ ability to be agile and adaptable, which enables them to correct course if circumstances or insights dictate doing so. Additionally, this approach tends to emphasize learning from both successes and failures through experimentation.
- Continuous learning and upskilling
The fact that these teams are always learning forms the basis for this final high-performing criteria: high-performing data teams proactively seek out new knowledge and skills. This means staying up-to-date on current tech as it evolves, assessing and implementing new technology as it emerges, and revisiting fundamental knowledge on a regular basis to keep themselves collectively competitive.
This is all key in the long-term as, with the right mindset, continuous learning can function as both holistic and restorative aspects of operative in complex, fluctuating data-driven environments.
Shifting your data team left for continued success
Clearly, high-performing data teams don’t happen by accident—instead, they’re a result of data leaders who can foster team agility, collaboration, and engineering excellence while helping individual team members stay locked in on delivering business value. But even the most skilled teams will still continually face challenges in maintaining data quality, governance, and alignment across departments.
This is why their continual learning should inevitably involve shift left data principles and, by extension, data contracts. When you implement them as part of shift-left data practices, data contracts help teams establish clear expectations between data producers and consumers, which reduces errors and improves reliability.
Are you looking for tools to help your data team operate at the highest level? Sign up for our product waitlist today to learn more about the role that data contracts can play in your organization.