7 Ways to Grow Your Data Team Without Breaking the Budget

As data continues to grow in importance across industries, organizations face a significant challenge:  building data teams with the right skills while staying within tight engineering budgets. 

To build teams, you need to hire professionals. But what if scaling your data team doesn’t necessarily mean hiring more people?

By taking advantage of the right tools, training, and strategies, you can scale your team’s capabilities and drive business value without drastically increasing headcount.

Challenges in the current data hiring market

Currently, the demand for skilled data professionals is skyrocketing. Whether it’s data engineers who can optimize complex systems, analysts who can extract insights, or data scientists who build predictive models, every role is essential. These in-demand professionals often command six-figure salaries, making the financial burden of scaling a data team substantial. For example, the average yearly salary of a data engineer is now $128,015.

The data industry is also experiencing a severe skills shortage. The rapid pace of technological change means that data professionals must continually learn new tools and frameworks to stay ahead. Traditional academic institutions are also struggling to update their curricula to match the evolving needs of the data market.

These issues lead to several pain points for organizations, including:

• Increased costs: Relying solely on hiring to fill the skills gap can lead to budget strain, as highly skilled data professionals are in high demand and come at a premium.
• Data flow blocks: Due to the skills gap, organizations may struggle to hire individuals with the necessary skills. Data teams then become the bottleneck in data workflows, resulting in slow data access and delayed decision-making across business units.
• Missed opportunities: Business teams may experience frustration as they are unable to get timely insights from data, resulting in missed opportunities in competitive markets.

Strategies for scaling a data team with budget in mind

Building a data team on a budget doesn’t have to mean sacrificing quality. With the right approach, you can scale your team, enhance skill sets, and deliver value without breaking the bank. Here are some proven strategies to do just that:

1. Upskill existing team members

Instead of focusing solely on new hires, it’s often more efficient and budget-friendly to invest in the development of your existing team. Upskilling your team helps fill the skills gap without the costs and time associated with hiring and onboarding new talent.

Providing training programs on emerging technologies, data visualization tools, or advanced data management techniques will allow your team to tackle a broader range of tasks. Upskilling empowers your employees to grow within your organization, and it fosters a culture of continuous improvement. For example, training business analysts to build basic data pipelines or giving data engineers tools to automate routine tasks can free up time for more strategic projects.

Investing in professional development not only fills the skills gap but also boosts team morale and retention, making your team more adaptable to future needs.

2. Encourage cross-department collaboration

A siloed approach to data development often leads to delays and miscommunication. Data engineers and business teams usually work in separate environments, creating delays and translation errors when tasks need to be handed off. Breaking down these silos and fostering collaboration between departments can lead to faster and more effective results.

Encouraging cross-departmental collaboration ensures that both business users and technical teams understand each other's needs and constraints. By providing shared tools where everyone can work together on data initiatives, communication becomes more fluid, reducing misunderstandings and speeding up delivery times.

Creating a collaborative environment allows teams to better align their efforts with business objectives, improving overall efficiency. This also reduces the translation errors that occur when business requirements are handed off to data engineers, ensuring that the right data solutions are developed the first time, every time.

3. Outsource or partner with consultants

Hiring full-time staff for every niche role may not always be the best approach, especially for specialized skill sets that aren’t required on a daily basis. Instead, consider outsourcing specific tasks or partnering with data consultants who can bring in their expertise on a short-term basis.

For projects requiring specialized knowledge, such as complex data architecture or data migrations, hiring a consultant can provide the expertise needed without committing to a full-time employee. Consultants are ideal for handling high-priority or one-off projects that require specialized knowledge. This strategy provides flexibility, allowing your internal teams to focus on more routine tasks while experts handle the more complex elements.

This approach helps manage costs by bringing in external expertise when needed, without long-term financial commitments. It also ensures that your team can tackle a range of complex issues with the right support.

4. Prioritize hiring the right roles

When hiring is necessary, it’s crucial to prioritize the roles that will have the most significant impact on your team’s performance and the organization’s overall data strategy. Not all data roles are created equal, and hiring strategically can maximize your budget and set your team up for long-term success.

Organizations often try to hire for every gap in their data team, but it’s essential to identify which positions are truly needed to meet business goals. For example, a company with a large backlog of data transformation requests might benefit more from hiring a data engineer who specializes in automation than from hiring a generalist data analyst. Similarly, roles like data governance specialists or data architects may be necessary to establish a strong foundation for scaling data efforts as the business grows.

By carefully assessing the needs of your team and aligning hiring decisions with those needs, you can ensure that each new hire will directly contribute to resolving issues and enhancing the team's ability to meet business demands. This focused approach prevents wasting resources on roles that are less critical to your team's immediate objectives and ensures that you get the best value from your hires.

5. Hire with a skills-first approach

When building a data team on a budget, prioritize specific skills over job titles. Instead of hiring broadly for roles like “data engineer” or “analyst,” focus on the exact skills your team needs to fill gaps and tackle challenges. To do this, you should:

• Assess key skills: Identify the specific skills that align with your team's biggest pain points.
• Hire for versatility: Look for candidates who can wear multiple hats. For example, a data engineer with automation skills can save the cost of hiring a separate automation specialist.
• Value problem-solving ability: Prioritize candidates who can quickly adapt to new challenges and contribute to solving business problems, rather than those with narrowly focused skills.
• Emphasize continuous learning: Hire individuals who are committed to staying current with emerging technologies, reducing the need for constant retraining.

This skill-first approach ensures that each new hire adds immediate value and can adapt to changing team needs, keeping your data team lean but effective.

6. Use automated data management tools

One of the biggest constraints for data engineers is dealing with routine, manual tasks. From data cleaning to integrating disparate systems, data engineers often spend a significant portion of their time performing tasks that could be automated. By automating repetitive tasks like data quality checks, pipeline monitoring, and even report generation, you reduce the load on your team while increasing efficiency. 

This not only saves time but also reduces human error, which is critical for maintaining data integrity. Automation helps improve the reliability of your workflows and enables data engineers to focus on optimizing systems and building scalable solutions rather than performing routine maintenance. The cost savings from reducing manual labor can be significant, and the added efficiency means your team can handle larger workloads without needing to hire additional staff.

7. Implement self-service platforms

Many data teams are bogged down by the sheer volume of requests that come from business units. One of the best ways to free up your technical team is to introduce self-service platforms that allow non-technical users to perform routine tasks.

By implementing self-service tools, you’re enabling business users to be more autonomous. These tools allow business teams to access and transform data independently without needing to know how to code or deal with advanced data pipelines. This speeds up response time and reduces bottlenecks, giving your data engineers more bandwidth to focus on higher-priority, strategic work. Ultimately, the right self-service platform reduces the load on your data engineers, streamlining workflows across your organization and enabling faster decision-making.

Reduce the need for costly hires with Prophecy’s self-service solution

Prophecy is an AI-native analytics and automation platform that helps organizations streamline and scale their data workflows. With our platform, you can also maximize the impact of your existing team, reducing the need to increase your headcount and improving team productivity.

Here’s what Prophecy does:

• Empowers business teams with self-service: Prophecy’s visual, low-code platform allows business users to handle their own data workflows, reducing the dependency on specialized data engineers for routine tasks.
• Automates data transformation processes: Prophecy automates complex data tasks, enabling your team to process data more efficiently and accurately. This automation helps scale your data operations without the need to increase headcount.
• Ensures governance and compliance: With built-in data governance, Prophecy ensures that business teams can work autonomously while maintaining compliance and data quality standards, which reduces the risk of errors and security issues.
• Fosters collaboration across teams: Prophecy enables seamless collaboration between technical and non-technical users, streamlining workflows and improving the speed at which teams can respond to business needs.
• Integrates with existing platforms: Prophecy integrates easily with your existing cloud data platforms, ensuring you can scale your operations using the tools and infrastructure you already have in place.

Learn more about self-service and how to enable it responsibly and at scale by watching our webinar, Reinventing Self-Service Data Preparation.