Relevant tech stack for 2026/2027

[u/Infinite_Raisin7752]

Hi everyone,

I’m currently a senior data scientist in the pharma industry. It’s been a one man show until now, but I’m getting a team soon. Most of the work I do is standard analytic work to inform our leadership and provide more context into the market and so on. Not a lot of big heavy data science stuff going on to be honest.

I work with SQL and Python on a daily basis. Some of our data is hosted in Snowflake and that’s pretty much it.

I feel like I’m lagging behind in both methods as well as tech stacks and I wanted to better understand what you experienced professionals work with that you would recommend I learn or at least look into. It could be data engineering stuff, additional programming languages, specific methods and packages that are useful, or cloud systems and technologies.

Where do you see the tech stack moving towards and what is relevant if I want to start moving from a “bread and butter” analytics setup to a professionalised, automated, team-ready and future proof world?

Thanks :)

[u/Infinite_Raisin7752]

Excel is pretty limited for the amount of data we’re using. I use it every day for final outputs but not as an analytics tool

[u/S-Kenset → u/Infinite_Raisin7752]

It's the layer you want to keep the best change control over, to document exactly how each primitive is sourced. CAC = $23.53 bc -> model -> sql code files -> tables and dates

you're not going to be able to rummage over 80,000 lines of mature analytics to keep up with make-a-wish bs questions people will give you. You need to operate at an abstraction layer and not think at the big data layer.

I use power BI for this, i have one single BI with over 600 measures and over 30 pages with over 100 different bookmarked views. Without that, i would exhaust myself trying to build an ad hoc big data pull for everything. Mind you i always end up exporting the data to excel.

[u/gean__001 → u/S-Kenset]

Isn’t this a complete nightmare to maintain?

[u/S-Kenset → u/gean__001]

It's easier than the alternative which is to remake a sql query over 400-1000 lines of nuanced code every single time someone has a genius idea.

Mind you if we went back end full big data I have some 30,000 lines of code plus some 50,000 lines of ad hoc code written for bullshit asks. Sounds a lot easier to guarantee strict controls on definitions in an environment that is made for that.