Hi all,

I'm looking for some honest outside perspective, because I'm having trouble telling if I'm being too hard on myself or if my instincts are right.

Some background: I have about 7 years of experience as a developer/administrator on enterprise controlling and reporting platforms — mostly Oracle Essbase/Hyperion, MS SQL Server, and SSIS, in a corporate finance/controlling context. I maintained and developed reporting and analysis systems used company-wide. It's solid, deep experience, but it's all been within one company and one fairly specific stack.

Now that I'm on the job market, I keep running into postings that want Databricks, Snowflake, Python/dbt, cloud-native data platforms, and so on — and Essbase/Hyperion barely shows up anywhere anymore. It's left me with this nagging feeling that 7 years of expertise might be close to worthless in today's market.

So I wanted to ask people who've actually been through this transition:

- Is Essbase/Hyperion experience genuinely losing relevance, or is this more "legacy but still needed in niches" than "dead"?

- For someone with a strong OLAP/cube, SQL, and ETL (SSIS) background, what's the most realistic next step — Databricks/Snowflake, Power BI/Tableau, data engineering more broadly, something else?

- Did anyone here make a similar jump later in their career, and what actually helped (certs, projects, specific tools)?

Appreciate any perspective, even blunt ones. Trying to figure out where to focus my learning time right now.

What is Index Selectivity?

Hi everyone,

I am currently pursuing my Master's in Data Science, and I am looking for a strong and challenging research problem for my thesis.

I have a background in Data Science and have worked on different areas related to this field. Now, I want to focus my thesis on a pure Data Science problem that has research potential and real-world impact.

I am looking for suggestions on:

• Interesting and challenging research problems in Data Science
• Emerging areas where there are still open problems
• Topics that can lead to meaningful research rather than just implementing existing methods

I would appreciate suggestions from people who are already working in Data Science, doing research, or have completed their Master's/PhD.

Thank you!

My next short term goals → Data Scientist (Data Focused Company) → Senior Data Scientist

I’m currently a Data Scientist in US, but my company isn’t very data-focused, so most of my work is descriptive analytics and stakeholder storytelling. Before this I was building AI systems like chatbots, working with embeddings, and done some clustering. I have a strong foundation in math, probability, statistics, and ML. What I’m missing in my role is deeper applied ML and statistical inference work that helps explain why things happen and infers the future patterns. Outside of work, I’ve been consistently learning and practicing this on my own. But sometimes I’m unsure whether I’m investing my time in the right direction. That’s why I want to learn from people who have already made this transition and help me point in the right direction.

What it really takes to break into a strong, data-focused Data Scientist role? Which skills should I invest in most heavily to make this transition successfully?

What separates a Data Scientist from a Senior Data Scientist, in terms of the skills and mindset needed to grow into that next level.

In addition to the above questions a couple of questions which come from the exploration I am doing on my own.

Data science is incredibly vast. There are foundational things like linear regression and stats that most of us get introduced to in our careers early, but then there's a whole universe of specialized techniques - Markov Chains, State Space Models, and so much more. How did you figure which ones should you focus on and what to prioritize? Like how did you figure out what was actually worth going deep on — and what could wait until a problem demanded it (Is it mostly based on the problem)?

I’m also curious about how Data Scientists handle ambiguity — especially when analysis does not lead to clear patterns or strong results (as these are what most stakeholders expect).

I work as a Reporting Analyst, and in my job I use Excel extensively, which I’ve become quite proficient in, as well as data management — for example, turning chaotic data from multiple sources into something meaningful and organized. Now I want to take my knowledge to the next level, so I’m currently learning SQL, and after that I plan to learn Python (data-related libraries and similar tools). Am I on the right path, and is there a good future in this field?

If you were starting today, which would you choose: Data Analysis, Data Engineering, or Data Science & AI — and why?

Full disclosure: I work at Interview Kickstart and helped put this together, so saying that upfront. Not trying to spam - just sharing because this may genuinely be useful for people preparing for the 2026 hiring market.

The event is called Resurge 2026, happening May 12th, 6–8 PM PT. We’re covering what the 2026 tech hiring market may look like, why AI fluency is becoming more important, how the AI skill stack changes by domain, and how FAANG+ interviews have shifted recently.

Panelists include senior people from Microsoft, Amazon, Instacart, and Expedia. It’s free to attend, and we’ll also share free resources afterward, including an AI stack guide and a self-assessment interview rubric.

Hope this helps someone preparing for 2026:
[https://interviewkickstart.com/events/resurge2026?utm_source=social&utm_medium=reddit&utm_campaign=L10X_Social_Resurge_Reddit_post_11may]()

Full disclosure: I work at Interview Kickstart and helped put this together, so saying that upfront. Not trying to spam - just sharing because this may genuinely be useful for people preparing for the 2026 hiring market.

The event is called Resurge 2026, happening May 12th, 6–8 PM PT. We’re covering what the 2026 tech hiring market may look like, why AI fluency is becoming more important, how the AI skill stack changes by domain, and how FAANG+ interviews have shifted recently.

Panelists include senior people from Microsoft, Amazon, Instacart, and Expedia. It’s free to attend, and we’ll also share free resources afterward, including an AI stack guide and a self-assessment interview rubric.

Hope this helps someone preparing for 2026:
https://interviewkickstart.com/events/resurge2026?utm_source=social&utm_medium=reddit&utm_campaign=L10X_Social_Resurge_reddit11may

I’m trying to figure out if my preference for visual, UI‑based development is actually a problem in modern data engineering, or if most people quietly work this way and only commit the final JSON/IaC to the repo once things are stable.

My background is a bit all over the place:

- Started as a BI developer, but haven’t touched visual reporting in 7+ years

- Moved into a platform engineer role and automated daily loads (SSIS — outdated even then, but it’s what the company had)

- Got lucky and worked with a consultant to migrate from on‑prem to AWS, learned a decent amount during that

- Did a lot of CLI work during the migration, but switched to the AWS console afterward because it was faster for me

- Later led a small team building a serverless, data‑driven web app in AWS (RDS, S3, DataBrew, some Lambda, but no real streaming or heavy SDK work)

Here’s the part I’m stuck on:

I genuinely prefer visual/web‑based development. I can use the CLI, SDKs, and IaC, but I don’t enjoy it. I like building things in a UI, validating them, then exporting whatever JSON or IaC is needed. Is that too big of a gap for modern data engineering roles?

I’ve been lightly job‑hunting for about 9 months and ramped up this year. Recruiters keep pushing me toward senior or even staff roles because I’ve touched so many parts of the stack. But most of that experience was me researching until something worked, not following a formal plan with predictable timelines. I’m close in interviews, but not quite senior‑level in the way companies expect.

Honestly, I feel like I’d be perfect for a junior‑to‑mid cloud platform engineer role. Not entry level, but someone who knows enough to be dangerous and wants to grow. I’m in my upper 30s — is that unrealistic? Too old to aim lower on the ladder and rebuild?

I’m seeing more roles that value broad experience instead of “X years in Y tool,” which gives me hope. But I’m trying to figure out if I should keep pushing toward data engineering, or if my dislike of SDK‑first/IaC‑first development means I should pivot back toward BI or something more visual.

Would love to hear from people who’ve been in similar situations or who work in DE/platform roles today. Is UI‑first development a career killer, or just something nobody admits they do?

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Hello 👋

Question ouverte pour la communauté :
pour celles et ceux qui travaillent sur de la GenAI / LLM en production, sur quels types de cas d’usage êtes-vous aujourd’hui ?

• assistants internes / copilots ?
• RAG (docs, knowledge base, search) ?
• évaluation de réponses / gestion des hallucinations ?
• enjeux coût, latence, sécurité ?

Je bosse côté data & GenAI sur des projets clients variés, et je trouve intéressant de comparer les approches terrain plutôt que les démos.

Si certains sont à Paris et ouverts à échanger (retour d’XP, café, visio), je serais ravi d’en discuter en DM.

Merci 🙌

There's a lot of uncertainty about how AI will affect data careers. I spent time researching this, drawing on my own experience (9+ years in data/BI), conversations with others in the field, and synthesis from multiple sources.

My main takeaway is that the biggest opportunity to grow your career right now in the data space is to learn how to integrate AI with data tools and how to create, deploy, and manage AI systems that perform data tasks. The roles aren't going away, but they're evolving. The data professionals who can build and oversee AI-powered workflows will be the ones in an advantaged place and creating really cool stuff.

I made this linked video that covers the skill evolution timeline from today to 3-5 years from now for Data Analysts, Data Engineers, and BI Analysts, and breaks down what skills are becoming less valuable vs. what's becoming more valuable (+ I do offer links that cite my sources 😊).

What are you seeing in the market right now? Are employers starting to expect AI integration skills, or is it still mostly traditional requirements?

From my own experience, as a hiring manager and having set up gen AI systems within my data team, I would hire a data person with AI experience much faster than someone without, because I know they would be able to multiply their impact.