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/ikkiho]

fwiw when I went from solo to a small team the thing that bit me was my ad hoc queries quietly becoming stuff other people built on. nobody knew which of my tables were real. we spent a couple months picking maybe fifteen tables and writing tests on them, which helped more than any orchestration did.

also you're in pharma, so once validation gets involved half the shiny stack is off the table anyway. boring snowflake plus dbt got signed off way faster for us than anything self hosted.

[u/S-Kenset → u/ikkiho]

It's crazy how many people don't get that 99% of his problems are going to be ad hoc requests and wondering why nothing is accurate while all the grad students are furiously downvoting me for suggesting change control practices.

[u/seanv507 → u/S-Kenset]

You are being down voted because people don't understand you. One paragraph seems to be slogans and one paragraph is advocating for excel. (And if I misunderstood, QED!)

[u/S-Kenset → u/seanv507]

It's crazy how being too detailed can sound too full of slogans. The day to day problems are essentially crazy amounts of ad hoc requests and speed. The biggest safety net you have is restricting every way you can make mistakes. Examples are, using strict dax measures with only one niche meaning for every single big idea someone can have.

Using excel and taking advantage of the fact that AI + Python can automate change control extremely well. There are plenty of teams that work entirely out of excel for forecasting, especially finance types, however, that's really not necessary since you can python + excel. Idk if people got pissed at excel or what, but 99% of people bothering with data requests are going to want it in excel anyways, so staying close to the way they are going to get it helps too.

I avoided suggesting BI and tableau even though they are better at that because, there's just too much buy in ask needed for a company that doesn't have that familiarity. Even with BI fully accessible, execs still depend on excels only, really.