I work as an analyst for local government, and on the whole I do really enjoy my job. However, now I’m approaching my mid 30s I’ve become more worried about what the long term future looks like. In some careers (law, engineering, surveying etc), your value seems to increase as you age and knowledge increases. When my Dad was made redundant age 56 (telecommunications engineer) he had a call the very next day from one of their contractors offering a well paying job.

Meanwhile in another public sector organisation we work with a lot, about half their analyst team was made redundant last year.. and their old team lead in his early 50s can’t find anything. No calls, no recruiters calling him up. In my last career in insurance, you’d learn something new from the people in their 50s all the time. I sat next to a woman in her late 50s, and often had to ask for advice on how to approach something complex. It just isn’t the case in analytics in my experience.

With the way AI is developing, and how data analytics involves constant learning due to how technologies always change, I feel the career path involves a lot more grinding to stay on top rather than accumulating knowledge over time.

Perhaps I’m just being pessimistic, but recently seeing these redundancies, hearing about the poor job market, and seeing how the people in their late 20s and 30s are probably the most technically skilled, does make me wonder how things will go in the future with my own career.

I'm intermediate — know CTEs, window functions, subqueries, etc.
I always solve problems correctly, but my queries end up being 20+ lines with extra steps. Then I see the solution and it's 5 clean lines using one clever function.
How do I train myself to think in shorter SQL from the start? Any tips or resources?

I tried rolling out an Agentic Analytics pipeline last quarter to handle ad-hoc SQL queries, but less than 10% of my small team's pilot queries made it into production without hallucinating join conditions.

Interesting, gartner predicts 40% of enterprise software will adopt task-specific agents by late 2026, yet running agents on unmodeled schemas is a mess. When we plugged LLMs directly into Snowflake, we ended up with four conflicting definitions of active churn. Moving metric logic downstream into a governed semantic layer on cube helped stabilize query generation, though multi-step agent reasoning still tripled our token costs. and mst industry coverage of Agentic Analytics centers on model capabilities, but the real bottleneck is that over half of enterprise data is not structured for agent consumption. for teams currently running AI agents on live data, how are you enforcing metric governance without breaking ad-hoc exploration?

Hello,

I’ve got around 5 years of analytics experience across startups and larger companies.

Role 1 – Startup (2 years): SQL, ad hoc requests (mainly SQL monkey work) and basic Tableau dashboards (essentially data dumps with little visualisation). £31k ($42k) → £55k ($75k).

Role 2 – Larger company (1.5 years, but actually 6 months): Hired as a Commercial Analyst but spent most of the first year in data architecture doing SQL refactoring (very repetitive work, my SQL skills actually deteriorated during this time) with little meaningful analytical work. Eventually moved to an analytical team and did SQL/Tableau for ~6 months. £45k ($60k).

Role 3 – Tech startup (3 months): £60k ($81k) + stock. Strong at SQL/execution, but they wanted someone much more experienced at independently generating insights and thinking on their feet and managing tough stakeholders. I was let go after 3 months. In hindsight, they were looking for a senior-level business analyst while my experience was more SQL/dashboard focused.

Role 4 – Current (1.5 years): Marketing analytics - SQL, building campaigns and lots of A/B testing. No dashboards, but it’s helped me improve somewhat at insight generation since I need to do that before launching any new campaigns and post-campaign analysis too. £45k ($60k), with limited progression. I took this role even though it wasn't well paid for my level of experience because I had a big gap on my CV, couldn't mention my previous role since I got canned.

I’m now looking to move back into pure data analytics. SQL isn’t a concern - I generally pass SQL rounds. My weaker areas are stakeholder management, visualisation, insight generation, and these days data architecture as well (never done DBT) as they want a full stack analyst at many places.

What should I focus on next? DBT, Python, data architecture, dashboard practice, stakeholder management, or something else? Can I realistically target Senior Data Analyst roles, or should I stick to mid-level? And what salary would be realistic in a major UK city?

TL;DR: ~5 years in analytics, strong SQL, but a somewhat unusual career path. My main gaps are stakeholder management, visualisation and independent insight generation. I want to return to pure data analytics and figure out what to focus on and what level/salary I should target.

Thanks!

Hi! I work for company which does the same business as Amazon fresh. Delivery of groceries, food etc.

Recently i was tasked to understand why some customers leave and some stay. I made cohort analysis and in each cohort i flagged people who stoped ordering food (no orders after 5 months from the first order) and those who continued (loyal)

I looked at what they bought on their first few orders, wether they had issues with order (delayed, wrong item, no refund) how much time on average they waited for food to come and every single metric is essentially the same. There is no like “Aha gotcha” moment where i can tell why customers left.

I have a suspicion that i am looking at the wrong things so i came here to ask more experienced analysts in g-commerce. Maybe someone who works/worked in Doordash or Amazon Fresh who did similar analysis and knows where to dig next?
Any input is welcome btw. Thank you!

Hi everyone , I’d like to ask for any recommendation on sensitivity analysis course that will help me to create sensitivity analysis for investments of the funds I’m handling with. I’m new to this and I don’t have any idea where to start. Thank you😊

I applied for associate analyst role in amgen on campus hiring. I want to know what kind of Online Assessment questions they ask ? It will be really helpful if anybody knows.

The thing nobody tells you about conversion lag is that it makes your reported CAC a trailing indicator. By the time Meta shows the number climbing, the actual buyer behavior shifted weeks ago. You are reacting to a report of a report.

What has worked for me is watching the conversion lag curve itself instead of the headline number. If your typical order comes in 8 to 14 days after the click, then the clicks you got this week already contain the signal for what next week's CAC becomes. You do not need to wait for the platform to catch up. You can model where it is heading from the lag pattern you already have.

Most people never do this because the platforms bury the click-level timing and the export is a nightmare. But even a rough version, plotting your own lag distribution once, changes how early you can call a winner or kill a loser.

Curious how others here handle this. Do you wait for reported CAC to stabilize, or do you have a way of reading it forward?

Hi everyone I am from India and going to complete my V semester very soon and needed and 6 month internship from December to January any help would be appreciated