A practical guide to building a high-performing data team, starting with the most critical hire and laying the foundation for long-term success and business value
Great post! As a data engineer turned data team lead this is exactly in line with my experiences.
Most businesses want the Data Scientist to jump on the AI bandwagon but have a terrible data infrastructure to start from. So they hire a data engineer.
But in reality they need a good data analyst to explore the data, tackle the low hanging fruit and create the first return on investments.
Saving this for the next time a company asks me for an "AI person" 😅
Great post - almost analogous to one way of getting into a DE role - I reaped the benefits of business acumen understanding data by starting as a data analyst before going into DS/DE!
I was going to do a post on this topic too, but I don't think I can top this (so maybe I will do it in six months or so and reference heavily lol). Love how detailed this is and how good the explanations are of each role! This is a must-read for any new data person out there!
Fantastic post! I am setting up a data team at work and we have Data Engineer and Data Analyst positions but no Data Scientists. We hired a Data Engineer first because we needed to get to the source data reliably and setup the pipelines and then the Data analyst to build the dashboards. In my view a lot of data visualisation work involves data modeling as well so dont really see a difference between Data Analyst and Analytics Engineer roles apart from the fact that the AE might be slightly more technical oriented whereas the DA is more business oriented.
I would also say that most B2B businesses might not require a Data scientist at all. The volume of data, the number of different data sources and the competitive environment might not justify such an investment in a B2B.
Great stuff, Rajat! It looks like you have set yourself for success.
Analytics Engineers are really a questionable role. With a team of good analysts, you may not need AE at all. Yet, this separation of concerns, provides more focus to your people, and that's why companies nowadays like to have two different profiles.
Sometimes thinking of "freestyle" Data Analyst could also help.
When I started working in data I was closer to data analyst and ended up building pipelines to enable myself and stakeholders to use BI assets in a self-served.
If you spot this personality type of analyst with builder capacities during interviews processes, you might end up with a better outcome in the short and also long term.
Great post! As a data engineer turned data team lead this is exactly in line with my experiences.
Most businesses want the Data Scientist to jump on the AI bandwagon but have a terrible data infrastructure to start from. So they hire a data engineer.
But in reality they need a good data analyst to explore the data, tackle the low hanging fruit and create the first return on investments.
Saving this for the next time a company asks me for an "AI person" 😅
I'm glad this resonated with you, Robin!
Great post - almost analogous to one way of getting into a DE role - I reaped the benefits of business acumen understanding data by starting as a data analyst before going into DS/DE!
I'm glad this resonated with your story. Thank you!
I was going to do a post on this topic too, but I don't think I can top this (so maybe I will do it in six months or so and reference heavily lol). Love how detailed this is and how good the explanations are of each role! This is a must-read for any new data person out there!
Thank you for being so kind. Yet, I know you can write a great article on the topic much sooner.
Fantastic post! I am setting up a data team at work and we have Data Engineer and Data Analyst positions but no Data Scientists. We hired a Data Engineer first because we needed to get to the source data reliably and setup the pipelines and then the Data analyst to build the dashboards. In my view a lot of data visualisation work involves data modeling as well so dont really see a difference between Data Analyst and Analytics Engineer roles apart from the fact that the AE might be slightly more technical oriented whereas the DA is more business oriented.
I would also say that most B2B businesses might not require a Data scientist at all. The volume of data, the number of different data sources and the competitive environment might not justify such an investment in a B2B.
Great stuff, Rajat! It looks like you have set yourself for success.
Analytics Engineers are really a questionable role. With a team of good analysts, you may not need AE at all. Yet, this separation of concerns, provides more focus to your people, and that's why companies nowadays like to have two different profiles.
Sometimes thinking of "freestyle" Data Analyst could also help.
When I started working in data I was closer to data analyst and ended up building pipelines to enable myself and stakeholders to use BI assets in a self-served.
If you spot this personality type of analyst with builder capacities during interviews processes, you might end up with a better outcome in the short and also long term.
Nice post! 🙌
That's true, Alejactro. I intentionally didn't mention people with your capabilities. People like you are more rare than unicorns.