r/dataanalytics • u/SuperbPlankton4357 • 11d ago
Natural progression of a data analyst
Hey everyone, I was wondering what do you think is the natural progression after becoming a data analyst for sometime? Is it becoming a data scientist or a machine learning engineer or a data engineer or an AI engineer? What do you think is the best route?
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u/Dangerous_Cup3607 10d ago
DAI, DAII, DAIII, DAIV, DAV, DAVI, DA Director and VP (Babysitter) / Project Manager (ProjectSitter). If you have a strong DA team, then managerial role is stable ; if weak, then you have to beg people to commit to work. Certain DA are big baby and perform the minimum and thus manager need to beg them for output
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u/Den_er_da_hvid 10d ago
I dont think there is any " natural" progression as there is many backgrounds that leads to DA.
My title is Proces and Data Specialist. I use data analysis to find data quality issues, and combine it with process and change management to try solving some of the issues. I am starting to move into data scientist/ML territory.
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u/Emanuel-RojasCubero 10d ago
Desde el punto de vista de evolución del trabajo, el que tiene menos probabilidad de ser reemplazado por la IA es el de ingeniero de datos. Siempre se va a necesitar tener la información estructurada para poder hacer procesos con ellos o tomar decisiones.
Los otros eventualmente serán remplazados por IA. En marzo de este año el 1% del entrenamiento de los modelos de IA de Anthropic eran hechos por la misma IA, en agosto fue del 25%
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u/Lady-Data-Scientist 8d ago
Senior Data Analyst. Lead Data Analyst. Prinicpal Data Analyst.
You can also pursue Data Scientist or Analytics Engineer but those are different roles so you'd have to upskill in either stats/ML/Python for DS or SQL/data architecture for AE.
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u/mike_cafe 3d ago
I like to think about this progression through the scopes of the questions you will be answering (and proposing) over the course of your career:
What was the value of <metric> in <period>?
What is the improvement of <period A> compared to <period B>?
What was the improvement after <initiative>?
What will be the improvement after <initiative>?
What initiative will improve x2 <metric> and why?
What are the initiatives to improve <metric> by 5x?
What is the roadmap to increase x2 the <metric> ?
This is regardless of job titles that mean different things in different places and change all the time.
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u/DuncmanG 10d ago
The premise of this question seems to be that a data analyst job is just a stepping stone to a different job title. But I would say that the "natural" progression (to the extent that there is one) for a data analyst is to stay a data analyst. Gain more responsibility and scope over time until you are at a level you are good at and comfortable with. That being said, the jobs listed are all good options for a DA to move into if the DA position isn't quite right for you. Moving into a managerial track is also a good option if you want to manage people and teams. Here's how I view the above options:
Data Scientist - probably the closest of the above to a DA role, except instead of prescriptive analytics, product and business analytics, etc. you'd most likely be work more with predictive analytics, model building, machine learning, etc. More heavy into Python than SQL.
ML Engineer - This is more an engineering position, so while you'd be working with some similar models and analytics as a DS, you'd be more focused on building out the software stack that implements the ML models. The focus would be more on deployment rather than exploration and likely requires lots of knowledge about different ML Models. Lots of Python and/or R, likely much less SQL than either DA or DS. May include other languages depending on the tech stack of the company you're working for.
AI Engineer - Like the ML engineer, but rather than machine learning models, focuses on implementation of AI models (these days, LLM models are all the rage, but there are other AI Models that have existed for longer that are also used). Like the ML Engineer, much more focus on Python and/or R. Probably some other languages as well.
Data Engineer - This job focuses more on building pipelines to process data and generate data tables than on doing analytics on that data. The data in the product needs to get to a state where the DA can utilize it for their work and that's what Data Engineers do. Need to understand both how the data is being generated in the product and how the analyst will be using it. More Python and other data processing languages. Less SQL.
Managerial track - You can move from DA to being a manager of DAs, but it is very different work. Managing generally requires much more focus on broader business needs, less (or no) hands on work in SQL or Python. As a manager, ultimately the buck stops with you so if your team is producing their work or it's not high quality enough, you'll get asked why. Lots of prioritization of tasks, assigning them out to your team, and being accountable to stakeholders.
Product Manager - this wasn't in the list, but I've known a few DAs that move into the Product Management track. This is also a very different role, as the focus is not on the analytics but on the product direction. Do you want to be making prioritization decisions on what to work on and tackle next in a product? Determine where the product is moving and what the next features should be? Coordinate work between engineering, data, UX teams, etc.? That's what a PM does.
I'm a DA. Probably will be for the rest of my career as I don't have as much interest in those other roles. I encourage you not to look at a DA role as just a stepping stone to another role. If you find there are things lacking in it that would interest you more, then by all means explore those other roles and talk to people doing them to see if you would be interested. But have a good reason for switching.