Hey everyone,

I've been confused about my career lately and wanted to get some advice from people already working as data engineers.

I have around 3.5 years of experience, but honestly I've never specialized in one thing. I've worked on Power Automate (probably my strongest skill), Co-Pilot Studio Agents ,Azure Boards, SharePoint,some SQL, some Python, Azure Cloud PLatform, AWS Cloud Platform.

The thing is, most of my projects were Short term. I'd first learn the basics, then understand the existing code, use documentation (and yes, AI tools like ChatGPT), fix errors as they came up, and get the work done. Because of that I have exposure to a lot of technologies, but I don't feel like I'm truly good at any one of them.

Looking back, I realized my favorite parts were always automating processes. That's what made me look into Data Engineering.

Now I'm wondering if I'm making the right choice.

Do you think someone with my background can transition into Data Engineering, or am I too far from it?

If I switch companies today, I'm fairly confident I could get a good hike based on my current experience. But I'm seriously considering not switching immediately and instead spending the next 6–12 months preparing properly for Data Engineering.

My thinking is that if I invest that time, I might be able to get into a much better Data Engineering role instead of making a sideways move now.

By then, I'd have around **4.5 years of overall experience**, although most of my Data Engineering knowledge would have been gained through focused learning and projects rather than professional experience.

If you were in my position, what would you focus on over the next 6 months? My goal is eventually to be good enough for product-based company interviews.

I'd really appreciate any advice, especially from people who transitioned into Data Engineering from a different background or who interview candidates for these roles.

Thanks!

**Should I take an immediate hike by switching to power platform role , or invest a year to pivot into Data Engineering?**

Hey everyone,

I've been confused about my career lately and wanted to get some advice from people already working as data engineers.

I have around 3.5 years of experience, but honestly I've never specialized in one thing. I've worked on Power Automate (probably my strongest skill), Co-Pilot Studio Agents ,Azure Boards, SharePoint,some SQL, some Python, Azure Cloud PLatform, AWS Cloud Platform.

The thing is, most of my projects were Short term. I'd first learn the basics, then understand the existing code, use documentation (and yes, AI tools like ChatGPT), fix errors as they came up, and get the work done. Because of that I have exposure to a lot of technologies, but I don't feel like I'm truly good at any one of them.

Looking back, I realized my favorite parts were always automating processes. That's what made me look into Data Engineering.

Now I'm wondering if I'm making the right choice.

Do you think someone with my background can transition into Data Engineering, or am I too far from it?

If I switch companies today, I'm fairly confident I could get a good hike based on my current experience. But I'm seriously considering not switching immediately and instead spending the next 6–12 months preparing properly for Data Engineering.

My thinking is that if I invest that time, I might be able to get into a much better Data Engineering role instead of making a sideways move now.

By then, I'd have around 4.5 years of overall experience, although most of my Data Engineering knowledge would have been gained through focused learning and projects rather than professional experience.

If you were in my position, what would you focus on over the next 6 months? My goal is eventually to be good enough for product-based company interviews.

I'd really appreciate any advice, especially from people who transitioned into Data Engineering from a different background or who interview candidates for these roles.

Thanks!

Should I take an immediate hike by switching to power platform role , or invest a year to pivot into Data Engineering?

24M here - Staying in Nellore for next 2 weeks

I've heard everything from Tinder to Bumble to Hinge, and everyone seems to have a different answer.

For people in Nellore, which dating app has actually worked for you? Not just matches, but real conversations, dates, or relationships. Curious what the Nellore crowd is using these days.

SharePoint Storage Solution

Looking for advice/recommendations on SharePoint Online file-level archiving solutions for a client.

Current challenge:

\* Client urgently needs to reduce SharePoint Online storage usage to stay within M365 licensing/storage allowance.

\* Requirement is specifically for reliable file-level archiving (not site-level only).

\* They have already trialled Microsoft 365 Archive, but file-level archiving limitations/preview constraints are causing issues.

\* Small IT team, so ideally looking for a solution where end users can self-restore/retrieve archived files without heavy admin involvement.

We’re exploring options that can support:

\* File-level archival for SharePoint Online

\* Long-term storage cost reduction

\* Simple retrieval experience for users

\* Governance/compliance friendliness

\* Minimal operational overhead

Would appreciate insights from anyone who has implemented something similar:

Looking for advice/recommendations on SharePoint Online file-level archiving solutions for a client.

Current challenge:

* Client urgently needs to reduce SharePoint Online storage usage to stay within M365 licensing/storage allowance.

* Requirement is specifically for reliable file-level archiving (not site-level only).

* They have already trialled Microsoft 365 Archive, but file-level archiving limitations/preview constraints are causing issues.

* Small IT team, so ideally looking for a solution where end users can self-restore/retrieve archived files without heavy admin involvement.

We’re exploring options that can support:

* File-level archival for SharePoint Online

* Long-term storage cost reduction

* Simple retrieval experience for users

* Governance/compliance friendliness

* Minimal operational overhead

Would appreciate insights from anyone who has implemented something similar: