r/analyticsengineering

Not sure if this is the right place to post this, but I honestly need some advice.

I graduated recently in Mechanical Engineering from NIT Warangal. If someone had asked me a year ago what I'd be doing after graduation, GTM Engineering would've probably been the last answer.

I started off learning data analytics because I enjoyed working with data. I spent months learning SQL, Python, Excel, Power BI, Tableau... hoping I'd get into an analyst role.

Instead, I got an internship as a GTM Engineer at a startup.

I almost didn't apply because I had no idea what GTM Engineering even meant. But I took the chance, and it turned out to be one of the best learning experiences I've had.

I worked on ICP building, cleaning and validating huge datasets, Apollo, Clay, Crunchbase, Serper, a bit of n8n automation, testing different ways of finding companies, figuring out why automations failed, and basically spending hours understanding companies instead of just ticking boxes.

The funny thing is... I actually enjoyed it. I liked solving those problems. I liked that every week I was learning something new.

Now the internship is over, and I'm back to applying.

And honestly... it's been rough.

I wake up, open LinkedIn, apply, send connection requests, ask for referrals, refresh my email, sleep, and repeat the same thing the next day. Some days I feel like I'm making progress. Most days it just feels like I'm shouting into the void.

I'm not someone with 2-3 years of experience. I'm just a fresher who happened to get exposure to a field that I genuinely want to continue in.

I'm looking for GTM Engineering, RevOps, GTM Ops, Sales Ops, Business Ops or even Data/Business Analyst roles. Bangalore, Hyderabad or remote works for me.

If you've been in a similar situation or know companies that hire freshers for these kinds of roles, I'd genuinely appreciate any advice or leads.

Thanks for reading.

Hi Everyone,

I have over 10+ years of experience in analytics. Last position was sr. data analyst.
currently not working from last 3 months.

worked on SQL Server for over 8 years. Did numerous analysis on jupyter notebooks. Automated reportings with OOPS python and VBA. 10+ years of experience in Excel. Build data warehouses for my own work. 2 months of exposure on Big Query in last company.

Want to transition to Analytics Engineer now.

Gaps:

1. Not worked on very larger data sets like Data Engenieers do. In interiews, first question they ask is "how much data have you worked upon?".
2. DBT - I am leraning - Jaffer shop project is almost 60% done, by learning and practicing with the book "Analytics Engineering with SQL and Dbt (Rui Machado)"
3. Airflow and Docker - Not started yet. (any book recommendations?)
4. GIT - very basic

Appreciate your responses

Thanks

I have been working on building a semantic layer using dbt in Snowflake. The biggest issue I have had is dealing with small ambiguity between business measures. I have built a testing agent in our ci/cd process to grade the semantic layer.

It just hit me today like a brick that er should train users, not just engineering users to use Plan mode with LLMs. When I turned plan mode on, any possible ambiguity came up in the plan and Claude asked me to clarify. I just wanted to share because I don't think using plan mode for business users is obvious.

Why do some teams actually use their analytics tools while others just ignore them?

I'm currently writing my master's thesis at RWTH Aachen on exactly this topic, and I could really use your help. If you've ever worked with dashboards, BI tools, reports, or analytics platforms, I'd be incredibly grateful if you could take 5 minutes to complete my anonymous survey.

👉 https://www.soscisurvey.de/trustindataanalytics/

Every response helps me a lot and directly contributes to my research. Thank you!

I've worked across different industries and the difference in how much people actually rely on analytics tools is honestly wild. Sometimes teams have access to the same tools and similar data, yet one team bases decisions on it while another barely opens the dashboard.

My own impression is that it often comes down to trust. I've even had coworkers tell us not to spend time building dashboards because they wouldn't use them anyway.

What do you think makes the difference? Trust in the data? Company culture? Training? Leadership? Tool complexity? Something else?

I'd love to hear your thoughts in the comments as well, but if you can spare 5 minutes for the survey, that would help me even more.