- Tags:: #📝CuratedNotes , [[Working in data|Working In Data]], [[Generalist vs specialist|Generalist Vs Specialist]] The specialization of roles in data teams is a mess, as explained by [[Erik bernhardsson|Erik Bernhardsson]] in [[What is the right level of specialization for data teams and anyone else.|What Is The Right Level Of Specialization For Data Teams And Anyone Else]] ## Core functions  *I don't remember the source of this pic* From [[Down with data science|Down With Data Science]]: ![[Down with data science#^2ca614]] ## Typical confusions / Special cases ### Data Engineering and Analytics Engineering Most of the work nowadays under the umbrella of [[Data engineering|Data engineering]] is in reality [[Analytics engineering|Analytics engineering]] (or they don't really have the idea of [[Data modeling|Data Modeling]]) because of the [[Modern data stack|Modern Data Stack]]. ### MLOps / ML Engineering A big chunk of what people call [[MLOps|MLOps]] or Machine Learning Engineering is in reality [[Data engineering|Data engineering]] (featurization, a feature store is similar to [[Metrics layer|Metrics Layer]]). See [MLOps is Mostly Data Engineering. • Kostas Heaven on Net](https://www.cpard.xyz/posts/mlops_is_mostly_data_engineering/). ### [[Business analysis|Business Analyst]] Oddly enough, you see companies in the wild with people with title and also people with the "Data Analyst" title. When you confront what is the different from those two, what you get is that these people may use data along with other resources but their job is to make recommendations to the business. What is the job of a Data Analyst then? **These two titles are one and the same.** Note that there is a super different definition of a Business Analyst closer to a Product Manager ([[Product Manager vs. Business Analyst]]). ### Digital/Web Analysts We also have [[Digital analysts|Digital Analysts]] , which could be considered an specialized set of Data Analysts both in terms of the tools they use (e.g., [[Google analytics|Google Analytics]]), and particular knowledge. Unlike data analysts, they are also responsible of capturing data with those specialised tools (along with front-end engineers). In fact, most of their time is usually spent there (see [[Digital analyst is dead. long live digital analyst|Digital Analyst Is Dead]]). They could be called "web analysts" too but eventually changed names because they started taking care of additional digital channels. ## Other interesting refs - [We the purple people](https://www.getdbt.com/blog/we-the-purple-people/?utm_source=pocket-app&utm_medium=share) - [Quasi-mystical arts of data & the modern data experience. - by Anna Filippova - The Analytics Engineering Roundup](https://roundup.getdbt.com/p/quasi-mystical-arts-of-data-and-the) > instead of building cross functional teams of experts in different areas, we ask our data professionals to be experts in all of them (and when this inevitably fails, we ask them to say no, a lot); instead of clear contracts between tools that enable decentralized ownership and reduce the amount of context a team needs to understand to be successful, **we ask our data professionals to be human interfaces, constantly negotiating a matrix of complex organizational dependencies.** > When the boundaries of your role and responsibilities constantly need to be negotiated, it can start looking like what you do is a lot of everything. “Wait, aren't you Ms. data generalist?” you may ask. Yes, yes I am. **This isn't a call for more role specialization, rather an acknowledgement that the way we distribute work on data teams is misaligned with the social contracts (i.e. the human interfaces) that underpin traditional software production (...)**. I also don't think the answer here lies with better tools, or better tools alone, as Erik suggests in his specialization piece from a few weeks ago — we need to be careful not to reinvent the wheel where well established fields of practice already exist (like UX). Instead, we should be thinking more about what are the right social contracts/human interfaces between the people who participate in the creation of a data product, and how do we best facilitate them.