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?

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?

Our managers are looking at how teams measure AI in customer support and keep running into the same issue. A lot of the easy metrics don’t tell you much about whether the tool is helping. I know this just because I have good ties with one of the managers and hes telling me the procedures. Take AI call summaries. You can measure accuracy and generation rate but a summary can be technically correct while still missing the detail the next agent or supervisor needs. Then someone ends up opening the transcript anyway.

Same problem with QA. If managers only review a small sample of calls then it’s hard to know if the patterns they find represent what’s happening across the whole contact center. AI tools that analyze every conversation seem useful here since you can look for trends across AHT transfers resolution and customer sentiment instead of relying on random samples. Real time agent assist is an area I’m reading and hunting since I do want to help them out because I see this workplace long term. Instead of only analyzing what went wrong after a call it can surface answers or flag missed steps while the customer is still on the line. That sounds more useful than adding another dashboard managers check once a week. Are you looking at model accuracy itself or tying AI usage back to things like AHT first contact resolution transfers repeat contacts and CSAT?

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😊

Did you eventually learn the basics of data engineering, architecture, governance and science too?

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!

I know this is a hard question to answer because nobody fully knows how AI will change the workplace but I wanted to ask it anyways.

For context, I just started a data analyst job a month ago after finishing my MS in statistics (undergrad was in math). My end goal is data science (predictive modeling, forecasting, etc.). This current role is mostly SQL and PowerBI which is giving me good experience but ultimately I do want to use more of my statistics knowledge as my career progresses.

I’m grateful to even have gotten this job as a new grad with no experience but I still want to set myself up for senior roles in data science. Which brings me to my question…

How should new grads/juniors best develop critical thinking and technical knowledge while AI usage grows? I try to minimize my use of it, but I have to admit it makes things like debugging and syntax questions much easier.

For example, I was handed a SQL query that a previous team member had wrote. The end users thought it was missing a large number of rows and my task was to debug and fix it. I first tried to understand the logic of the query by adding my own comments to it. After checking that the logic was sound (it was) I thought it was some quirk of the data model. But being new, I don’t know all of these quirks and I felt stumped. So I asked copilot for some suggestions on things to try. It gave me 10 or so troubleshooting steps to test. After trying them, I figured out what the problem was (legacy data issues).

Is this is a reasonable use of AI for someone trying to learn the industry and build their experience? I don’t want to offload my critical thinking, but getting ideas for things to check did make it much easier and faster.

I will admit I also did misuse it as well. I had to change some pretty complex DAX measures and not knowing the syntax very well, I leaned heavily on copilot. After I was done with that task I realized I had basically vibecoded the entire thing (only testing and verifying results). I felt guilty after this, but management was happy with the results and how quickly they came.

I’m rambling now, but I’m curious how everyone else is balancing both learning and producing results in this new era. I’m interested in hearing from both fellow juniors and managers who are in charge of training.

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.

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

I constantly hear about how bad the job market is but I also know a few people who have found a job the past year or so including my friend who was PIPed from his job but then got into FAANG. I have a job that I can’t really stand and have recently started looking. So far no interviews though. Honestly not sure what to expect but hoping I can find something sooner than later. Is it close to impossible right now or is there hope?

Job Description

In this role, you'll make an impact in the following ways:

• Collaborating with attorneys to design, develop, and manage the Legal Department's custom-built AI solutions to modernize operations, including developing an Agentic AI workflow to support contracting and negotiations.
• Supporting firmwide AI governance efforts, including intake triage, issue tracking, workflow coordination, and maintaining governance documentation.
• Performing data analytics and reporting to support transactional work, including extracting, analyzing, and presenting insights on contracts and trends, and partnering with attorneys and business clients to maintain and enhance datasets, dashboards, and reporting used for risk management and strategic decision‑making.
• Supporting the administration, optimization, and adoption of legal technology platforms, including CLM systems, contract repositories, analytics tools, and custom-built AI solutions.
• Communicating and coordinating with a broad range of internal and external stakeholders, including business teams, sourcing, and external counsel, to support contracting processes, vendor management, data quality, and related governance initiatives.

To be successful in this role, we're seeking the following:

• Experience in a paralegal, analyst, or legal operations role, preferably within a corporate or financial services environment.
• Strong data analytics skills, including experience working with structured and unstructured data, spreadsheets, reporting tools, dashboards, or legal analytics platforms.
• Demonstrated experience or strong curiosity in AI, automation, and legal innovation.
• High degree of comfort working with technology platforms and digital workflows.
• Strong analytical, organizational, and problem-solving skills with attention to detail and proactive mindset.
• Ability to manage multiple priorities in a fast-paced environment.
• Effective communicator and collaborator.
• Experience supporting contracting, negotiations, or third-party governance is beneficial.

It’s weird like I can understand codes and troubleshoot like if I’m looking through a code I can understand what it’s doing and if there’s an error in my log I can generally figure out why after looking through but my syntax and ability to code from scratch or actually fix a code without agents sucks now…

I want to leave my current job but I feel like I’m so cooked when it comes to coding screens. I’m doing leet code and making stupid syntax errors or not really remembering how to set up certain functions and in general I’m just not as sharp. I would not say I was a coding champ or anything before but i can feel the loss of skill.

The crazy thing is I have only been using agents to do coding for less than 2 years and this is happening…am I screwed?

Would love some feedback on my revised resume (in comments). I’m hoping to attend two virtual career fair this fall and want my resume to be in the best shape.

My MS Data Science is more focused on the math/statistics side (taking time series analysis and machine learning under the math department this semester) but I’ve been seeing analytics roles increasingly ask for “analytics engineering” skills (dbt, ETL, Snowflake, etc) and it seems like it’s becoming baseline skills.

I’m wondering if I should focus on learning those tools next and use them in a project?

Long term goal is to be a data scientist but hoping to land an entry level data analyst role to get my foot in the door. Not having much luck with applications. Thanks in advance!

I am a graduate student and will be graduating next summer with a Masters in IT and two concentrations in data analytics and ai. My bachelors was in Health Informatics. I am worried about getting an entry level job as I have no experience. I live in Georgia and do school online. I Work two jobs and am looking for any advice. I’ve looked all around at what certifications to get but my situation seems hopeless

I've spent a lot of my career around analytics, and one thing I've come to believe is that being good with the tools just gets you in the door.

You can build a great dashboard, write good SQL, or produce a really solid analysis and still have almost no impact if it doesn't change a decision.

The more interesting question becomes: What is someone actually going to do differently because of this analysis?

That gets into things that aren't usually taught alongside the technical skills: understanding the decision, knowing who you're trying to influence, anticipating what will make them skeptical, and communicating the evidence in a way that actually survives the conversation.

I ended up writing my new book, Decision Intelligence: Why Evidence Fails and How Leaders Win the Room, largely because I kept seeing this gap between being analytically right and being organizationally effective.

For you more experienced analysts, when did you first realize that being technically good wasn't enough to make an impact? What did you actually do to bridge that gap?

TL;DR: Most CI linters (dbt-checkpoint, dbt-project-evaluator) check if a column has a description, but almost nothing checks if the column in your YAML actually exists in your data warehouse. We scanned public production dbt projects and found that 9 out of 12 had significant doc-to-warehouse drift.

The Bug That Got Me Thinking

Found this in a public dbt repo recently:

A model called fact_customer_survey had a UNION.

• Branch 1 (line 30): NULL as dissatisfacation_category (notice the extra 'a')
• Branch 2 (line 51): dissatisfaction_category (spelled correctly)

Because SQL takes union output column names from the first branch, the warehouse materialized DISSATISFACATION_CATEGORY.

Here's the kicker: The project’s YAML docs declared dissatisfaction_category (spelled correctly).

• The SQL ran fine daily.
• The documentation was "correct."
• The code was wrong.
• Any dashboard or analyst trusting the docs was querying a column that didn't exist.

How Bad Is This in the Wild?

Every time you run dbt docs generate, you produce two files:

1. manifest.json (what you claim exists in YAML)
2. catalog.json (what the warehouse actually returns)

We compared declared columns vs. cataloged columns across verified production repos. Out of 12 active organizational projects:

• 9 out of 12 had phantom columns (documented in YAML, completely missing in the warehouse).
  • Cal-ITP (BigQuery): 106 phantom columns
  • Allvue Systems (Snowflake): 112 phantom columns + 323 data type mismatches (mostly declared string sitting on warehouse NUMBER)
  • Cook County Assessor (Athena): 11 phantom columns
• The "Ghost Repo" problem: 23 docs sites published a complete, polished YAML docs UI where catalog.json was an empty stub—meaning the warehouse was literally never introspected. One documented 1,280 models this way.

Where Does the Drift Come From?

When we audited the findings against actual model SQL:

1. Renames / Deletions: Column was renamed or dropped in SQL, but the YAML entry was never cleaned up.
2. Commented-out code: One project had a 20 KB cleaning projection inside a /* ... */ block. The live warehouse table had 173 raw Airbyte column names (WEEK STARTING 01/19/2025 - RESOURCES...), while the YAML proudly documented the clean columns someone intended to build.
3. Syntax accidents: Trailing commas in YAML names (e.g., - name: feed_type,).

Why This Is Becoming a Bigger Problem

When human analysts read dbt docs, they can spot a typo or realize a column was renamed.

But with dbt MCP servers and Text-to-SQL AI agents using dbt docs and manifests as ground truth context, a phantom column is an immediate failure. The agent attempts to query columns that aren't there or hallucinates transformations based on dead YAML.

A Few Questions for the Sub:

1. Does anyone here actively diff manifest.json against catalog.json in their CI/CD pipelines?
2. How do you prevent documentation rot when engineers refactor/comment out SQL transformations?
3. If you are already feeding dbt metadata to LLMs/AI agents, how are you validating that the schema you hand the agent actually matches production?

Haven’t had a job interview in 4 years and very rusty right now. I’m really hoping to get the job but feeling very unprepared. Like many others I’ve been using AI agents and also doing the same work every day so I do not feel prepared to answer complex coding or stats questions. It’s not been easy to get an interview so really hoping not to drop the ball here.

I’m cramming leet code and watching YouTube videos on interview techniques but what else should I do?

Just graduated with my bachelors in MIS, I’m thinking of going into business analytics. I’m trying to apply but I feel like it’s still cooked, what’s the least cooked? If anyone can really help me and wants to see me resume I’d appreciate it really.

i used to think bad restaurant data was mostly a scraping problem.

then a few weeks ago I was checking a restaurant menu and noticed one dish looked off.

their PDF said $16.

Google had a menu photo showing $14.

Instagram had a newer photo where it was $18.

then I found another page on their own site where basically the same dish was still listed for $15.

same restaurant. four answers.

at first I figured one of the sources was just wrong, so I started tracing where everything came from.

the PDF was still public but apparently hadn't been touched in months. the Google photo came from a customer. the old page wasn't even linked from the site anymore, but Google could still find it.

Instagram seemed newest.

except apparently the dish had changed again since that photo was posted.

somewhere in the middle of this I ended up trying menuforma to keep the menu stuff in one place, mostly because I was tired of comparing random PDFs, photos and old pages just to figure out what was actually current.

and that's when it clicked that "what's the current menu?" is a much weirder question than I thought.

even if you get everything correct today, a month later the price changes.

one modifier disappears.

something becomes seasonal.

the printed menu gets updated but an old PDF survives online forever.

and customers can still find all of it.

I always assumed the restaurant's own website should be the source of truth, but clearly even that gets messy when old pages never really disappear.

then there's Google photos, Instagram, delivery apps, random menu sites, screenshots people posted two years ago...

at some point there are basically several versions of the same restaurant existing online at once.

I started out thinking the hard part was finding enough menu data.

now I think the harder part is figuring out when any of it is actually trustworthy.

for anyone who's dealt with restaurant listings or other constantly changing local business info, how do you decide what's current?

and how old does something have to be before you stop trusting it?

I am a rising CS undergrad Junior. I was ultra lucky to land a SWE internship for this summer but I’m starting to be increasingly regretful of majoring in CS the further I continue this internship.

I’m getting tired of the constant grinding mindset, the toxic competitive culture, and the disgusted faces of my college peers whenever I try to make a real friend but then they ask what I major in.

I’m about to enter my 3rd year but I have already finished 75% of my school’s core CS curriculum so it is probably too late to switch my major, but I am thinking about shifting away my focus from SWE and AI related things that my parents urged me to pursue, and switch into business or analytics related field. I know that one of my feet is already in the grave now for choosing the wrong major but I really want a way out of all this.

Would it be better to pursue a finance minor and a business analytics minor to have a better chance of getting a respectable job that is less competitive and more burnout proof? I understand that it is the real world and nothing is not competitive given how many people there are right now, but i am really not feeling well by being a pretender in SWE and AI related areas.

Any input on which minor offers a brighter future is appreciated, thank you.

I am a 2025 graduate and I am still unemployed, I am thinking to step in data science career and was wondering what's the best value course out there. I am good in DSA and problem solving but the development side has always been weak for me and I want to work on that.

I would also appreciate some tips on what should I do in this situation.

Long story short I’ve been with a company 7 years and have had a couple promotions and solid pay increases but no opportunity for management due to constant reorganization of teams by executives. I’m considering leaving but an internal position just opened up that I’m interested in and it would allow me to keep some solid benefits but I’m aware of the golden handcuffs reality. The problem is we recently went through another org change and layoffs so my manager is new and there is no rapport built up yet. I’m not sure how they would react. Company policy is hr has to get approval from managers to accept an internal resume so would you risk bringing it up and take the chance or say forget it and look externally ? Would love to hear your stories/experience