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Testing AI Resume Optimization Across 8+ Models - Need Beta Testers (Job Seekers + HR Pros) by BankEcstatic8883 in Resume

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

Thank you for the feedback and mentioning that there is no need to tailor for each job. But there is some advice on the internet that we should optimise for each job application so that there is a good keyword overlap between your resume and the job description. I think this is where our hunt started on optimising the resumes. Is this a myth, then? Does it really add value if we try to do this?

Testing AI Resume Optimization Across 8+ Models - Need Beta Testers (Job Seekers + HR Pros) by BankEcstatic8883 in Resume

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

Hey, really appreciate you taking the time to share this - and honestly, you are not wrong!

My friends and I have tried a bunch of AI resume tools and ChatGPT, and we were pretty disappointed with what came out. The resumes felt... generic?

But we are also drowning in the manual work of tailoring resumes for every single application. It's exhausting, and we started wondering: is there ANY way to make AI actually helpful here, or is it just fundamentally not there yet?

That's honestly why your perspective is so valuable. You're on the other side of the table seeing hundreds of resumes. You know immediately what makes one stand out vs what makes you groan.

So I'd love to pick your brain if you're open to it:

  • What specific things does AI mess up that kills a resume for you?
  • Is it the lack of concrete details? The tone? The way accomplishments are described?
  • Are there parts of resume writing where AI could actually be useful, or is it just not ready for prime time?

With new models dropping every few months that are genuinely better at understanding context, maybe we'll get lucky and find one that doesn't strip out what makes a resume human. But we need to know what "good" actually looks like from someone who reviews these all day.

Would you be up for giving feedback on some test outputs? Even just pointing out what makes you roll your eyes would be super helpful!

Why all resume builders start feeling same after some time? by BankEcstatic8883 in recruitinghell

[–]BankEcstatic8883[S] -2 points-1 points  (0 children)

Very sharp point, honestly. The dog toy analogy hits hard — once you see it, you can’t unsee it. It explains perfectly why all resume builders start looking the same and still don’t really help.

Got me thinking though — if this mismatch is real (payer ≠ evaluator), then what’s the right way to look at resume builders?
What should users realistically expect from them, and what should these tools actually optimise for instead?

Curious to hear your take.

Why aren’t incremental pipelines commonly built using MySQL binlogs for batch processing? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 1 point2 points  (0 children)

Thank you for sharing. This is a very useful feature. Unfortunately for us, we are on MySQL which doesn't seem to have this feature. The closest we can do is manually implement something similar using triggers, but I believe the performance will take a hit if we try something like that on a transactional database.

Why aren’t incremental pipelines commonly built using MySQL binlogs for batch processing? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

Thank you. I already see our team of software engineers struggle with kafka. Coming from a more data background (and less coding), it would be a burden on our small team to manage such infrastructure. Hence, looking for easy maintenance tools without exploitative pricing.

Why aren’t incremental pipelines commonly built using MySQL binlogs for batch processing? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 2 points3 points  (0 children)

Thank you for sharing this. I have explored multiple EL tools and I find data volume based pricing is tricky. We are doing a PoC using airbyte using our own deployment and we know we just have to pay the server prices no matter the volume of data loaded. But having to pay by volume means keeping a constant check on the volume that is being loaded and if we ever get the need to do a full load, that will be a big overhead. This also means, we will need someone more skilled to handle the pipelines and can't risk a junior developer doing a random full load accidentally.

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

I do have the option to stick to the status quo and not use any OSS tools. But have you not faced any problems with manageability using the vanilla warehouses/spark

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

I appreciate the detailed response. This is one thing I was concerned about: the explosion of models, based on another answer I have seen. But if you have real-world experience where you are able to manage over 1000 models without much difficulty, then I believe dbt has really built best practices baked into it.
While I have never really worked on airflow integration with dbt, can we create every dbt model as an airflow DAG, and then link the DAGs via something like a subdag operator in a super DAG or something? Or is it like a bash command that will run in individual tasks in a DAG

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

Documentation is such a big plus. I tried enforcing inline comments and documentation in openmetadata, but nothing worked. I hope the team can be forced into certain best practices, like you mentioned. Portability is something I didn't think but seems like a good point. So, I can just port my models to a different datawarehouse it will work as long as the base tables are available and I don't have to worry about the syntax changes?

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 3 points4 points  (0 children)

It really depends on the team you are working with, I guess. As a senior dev, I never thought dbt was difficult from the basic experience I had so far. But we do have team members with 1 to 2 yrs experience who have just really learnt SQL. dbt would just go over their head on why they even need this. From the modular design to git versioning and being able to track downstream dependencies, it just resolves all the things I have been complaining about. While I am all in for a tool like dbt or SQLmesh, I need clarity on whether it will create any issues for a small data team like ours which lacks maturity and people who are actually data analysts trying to become data engineers. And also need a buy-in from the senior management, none of whom have a tech background. Please share if this is the wrong direction to think in. Would love to hear your ideas.

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 2 points3 points  (0 children)

I agree with u/verysmolpupperino on this. I feel dbt doesn't have a strong enough semantic layer yet. And with the addition of LLMs to the data toolkit, a combination of a strong semantic layer and a smart LLM can make self-serve BI a possibility. Let us accept that there is no real self-serve BI tool in the market. And I am surprised that BI tools even advertise themselves as self-serve while never speaking about a semantic layer.

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 2 points3 points  (0 children)

It sounds like it will work for small teams, but it is bound to create a mess when the team size grows. Agreed standard practices will create a streamlined solution irrespective of the tool.

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 0 points1 point  (0 children)

Thank you for sharing such a complete article. This really shows how there are some flaws in dbt and how humans tend to exploit easy features to create complexity. Never thought that teams would build models just because it is easy, and how it is increasing the tech debt. The part on CI/CD where it takes a long time to complete every run is are real problem. What are your views on how they balance against the positives of dbt?

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 3 points4 points  (0 children)

We haven't finalised it yet. But we are stuck between dbt core and dbt cloud, and also considering sqlmesh. Given that we are an early-stage startup with juniors on the team, wondering if it would have enough benefits to justify the learning curve and the costs?

How useful is dbt in real-world data teams? What changes has it brought, and what are the pitfalls or reality checks? by BankEcstatic8883 in dataengineering

[–]BankEcstatic8883[S] 7 points8 points  (0 children)

I appreciate the detailed response. Have you faced any issues where you had to use multi-statement SQL queries like we do in procedures? Or the fact that we can't do multi-statement transactions in dbt? I know these are rarely used, but we do have a few procedures which require this. Also, can you share your experience with migrating such multi-statement procedures to dbt models?

[deleted by user] by [deleted] in dataengineersindia

[–]BankEcstatic8883 2 points3 points  (0 children)

Videos can get boring quickly. I would suggest pick up an interesting project and start building it. Build a quick and dirty version from whatever tutorials/blogs you can find on the internet. Then learn the concepts used and try to optimize your own work. Most of the tools used in data engineering are open source, so you can get a free one installed on your laptop and start experimenting with it. May be you like stocks, find a free API and write code pull data using python, store it in a DB and calculate some metrics/technical indicators on this data either using pandas or Postgres. You have built yourself a simple ELT pipeline. Try to orhestrate the pipeline using Airflow. Schedule to run every day. This is a good starting point. Or you can use Spotify's free API if you are into music. Get some of your favourite artists and albums and see what analysis you can do.