In our experience at Secoda working with many data teams, we've seen most data teams do not have the tools they need to succeed. For growing organizations, the data function is usually an afterthought. The first data hire is brought on before raising a Series A and is expected to manage the workload that comes afterward with little to no support.
One reality that many companies face when adopting cloud technology: designing data infrastructure for business in a cloud computing environment is just different. Legacy stacks can indeed suffice for many companies. But as business requirements grow and use cases increase, both in number and complexity, the models and assumptions that worked well enough in the data center become problematic.
If you have spent any time in the data space in the last 10 years, you'll know that data job titles have gotten hilariously complicated and confusing. There are Data Analysts, Data Scientists, Analytics Engineers, Data Engineers, Business Analysts, Business Intelligence Analysts, Product Analysts, Product Data Scientists, Data Product Managers (?), ML Engineers, Data Enthusiasts, People Who Just Really Love Counting, and dozens of other titles floating around.