Business intelligence teams are made up of data professionals with a variety of different skills and backgrounds. The people on these teams generally work within specific domains of the BI platform. Early teams may only need one experienced BI professional to begin launching their implementation, but more established teams will necessarily find themselves in need of individuals with a deeper level of specialization into more specific roles. Depending on where exactly your team finds itself on its BI journey, your needs for help will differ. Knowing who you should look to hire can be considered a skill in and of itself because there are so many different roles within successful BI teams. There are also some high-level considerations to keep in mind when hiring a data professional to guarantee you have found a good fit for your BI team. Whether you are just starting out or looking to grow and scale your team, this guide is for you.
First and foremost, when hiring for any BI role, your team should consider how well the candidate fits at the:
1. Individual Level
2. Team Level
3. Work Level
The individual level for candidate fit is important because you want to make sure you are hiring the right person for the company. This person should have the right technical skills and personality traits for the job. The skills this user needs depends on the role, but they can be hard skills like programming, visualizing, and dimensional modeling — or they can be soft skills like storytelling, communication, and intuition. Make sure you are hiring the right person for the job. The soft skills in analytics can sometimes be more important than the hard skills, depending on the role, because collecting requirements from stakeholders and sharing analytics back out can be challenging to communicate in an easily digestible manner.
The team level fit is important to identify because you want to make sure you are hiring someone that fills a missing hole in your teams’ skillset. Look for skills that your team is weak in and find a person who fills these holes or complements the existing skills of the team. The BI team is only as strong as its weakest link and if you are hiring someone to own a piece of the BI pipeline, you want to make sure that they are going to seamlessly integrate with and improve your teams’ capabilities. Look for individuals with strong BI skills that are going to bring a new dimension to the team and help others on the team grow.
A work level fit is the final thing the organization should consider. Work level fit is making sure that the person or role you are hiring for is moving the BI team in the direction that the business most needs the team to move towards. For example, if you have not maximized the insight the team can provide to the business at the descriptive and diagnostic level, do not hire a data scientist earlier than necessary for insight at the predictive level. You could likely use another role to unlock more of these types of insights before you look to bring out the power of machine learning for the organization. Make sure that the person you are hiring fits with the overall objectives for the organization, so you get the most out of the BI team to support your company’s decision makers.
Once you’ve confirmed the person is a fit, it’s time to consider the role they’ll be filling. The most common roles in established BI teams are:
Business Intelligence Analyst
Business Intelligence Developer
Data Engineer
Analytics Engineer
Data Scientist
The business intelligence analyst is usually the more outward facing member of the team who operates as proxy between the BI or analytics team and other business stakeholders. A company just getting started in BI might look to launch their BI team with one highly skilled BI analyst who is experienced and understands the entire process for how to establish a BI platform from scratch. This BI analyst should be passionate and creative when dealing with data. They should have a strong sense for how to provide the insights business users will need. The BI analyst should understand how to create a BI platform that is automated, scalable, and insightful. This role should look to establish process where no process previously existed so that the team can inevitably scale for more data and for additional team members. They ought to combine soft skills with technical skills to effectively collect project requirements and to deliver results back to stakeholders. The combination of their technical skills and data architecture background with strong communication skills will make them a great investment when launching your BI initiative.
A business intelligence developer is like the BI analyst but oftentimes more technically specialized. Your BI analyst will likely be more business facing while your BI developer may be more focused on the implementation and execution of the analytical tech stack. They will be data warehousing experts who can also create reports and implement some data pipelines to help feed into the data warehouse. Their primary home, though, will be inside the data warehouse. They should be strong SQL experts who are comfortable with the Kimball dimensional modeling methodologies. Trust this user to implement architecture that will enable you to get the most with the data that lives inside your data warehouse. They will be able to work with reporting and analytical engineers to help create a seamless reporting experience while also working with data engineers to get the data inside the data warehouse that they need.
The data engineer is your team’s data pipelines expert. This individual automates the data collection process and gets all your necessary data consolidated within your data warehouse. This role may have some crossover in scope with your BI developer if they are capable of some of the dimensional modeling as well. You should make sure your data engineer is involved in as much of the scoping process as possible so that they are familiar with how the business intends to use the data. Knowing this, they can implement pipelines that refresh your data as frequently as is needed for analysis, and model it in a way that is most easily accessible for visualization. This role is likely to be one of the most technical on the team. The development of the company’s data pipelines will require the data engineer be extremely familiar with software development to create custom, programmatic solutions. A successful data engineer will have a strong development background, so make sure your primary engineer has the right experience.
Your team’s analytics engineer will work with cleaned and modeled data that sits at the end of your data warehouse. They might also collaborate with the BI developer to handle the dimensional modeling side of things from within the warehouse. This role’s bread and butter will be working on visualizations that effectively communicate your business processes back to your company stakeholders. They know how to share data in a way that is highly interpretable to the consumer. They work in Power BI, Tableau, and other data visualization tools. Your analytics engineer understands that BI is not just pretty graphs and is all about tracking and understanding the changing nature of your business processes.
The data scientist is the machine learning specialist on your team. They blend mathematical and statistical principles within programmatic solutions to provide foresight for the company. A data scientist will enable your BI team to leverage existing company data to create new data. This role is one of the most exciting on the team because it gives you an even higher competitive advantage than BI already offers at the more descriptive level. The insight that this role can provide your team is particularly powerful and you will have to see it to believe it. Make sure your data scientist has a data engineer paired with them so that they have the necessary data they need available to them.
Knowing who to hire for the job and ensuring that they are a good fit will make or break a BI initiative. Bring these data professionals aboard the team, make sure they have the support and tools they need, and help them succeed by asking the right questions and offering them companywide endorsement. When the BI team succeeds, the business succeeds.
