I am looking for one or more partners to start learning and doing Data Engineering lessons and projects. My time zone is in Toronto.

Hi, I quit my job without an offer. I want to get an offer by January. If your company is hiring or looking for a Data Engineer. Do let me know!
And any referrals would be appreciated!

Thanks in advance!

I am in 3rd year preparing for DE roles but rarely any internship. I have DS projects too but similar situation having hard time getting info.
For Projects working on a Bigger DE project will put it on resume when at stage to be able to present.
Any advice on what to study or where to apply anything at all will be helpful feeling stuck

Hi guys,

I have cleared thoughtworks interview rounds in Data engineer domain/role.

Just wanted to check on the data engineer project availability in thoughtworks.

Can anyone please provide me some suggestions or share any other thoughts if any.

Hello everyone, I’ve been looking into Data Analytics for quite some time now, but I’ve decided that I want to move into a more DE role that goes long with my degree.

This is my current experience, and I was wondering if I should add anything. Or any recommendations on what I should learn and be more prepared?

Any brutal honest is appreciated!

Thank you and have a great day!

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I’ve been applying for Data Engineer roles through LinkedIn, company career pages, and other job portals, but I’m wondering how effective direct applications actually are.

For those who have changed jobs recently, did you get interview calls by applying directly, or did most of your calls come through referrals?

I have around 4–5 years of experience in data engineering, mainly with Azure Data Factory, SQL, Databricks, and PySpark.

I’d like to know:

- Have you received interview calls through direct applications recently?

- Are referrals significantly more effective?

- Roughly how many applications did it take for you to get a callback?

- What strategy worked best for you?

Would appreciate honest experiences, especially from people working in India’s IT industry.

Hi gang,

I have recently completed my data science masters from reputable university but i am stuck some where to transit into a professional careers.

I have two foundational certifications like Aws cloud practitioner and azure fundamentals and I am preparing for fabric associate certification for a week now and i have several projects around data engineering in open source unlike using paid ones like fabric.

So where do i need to focus? What should i learn more? Why am i stuck ? And learn and giving fabric associate certification is worth doing ? Or am i wasting time ? Have lot of things in mind …

And i am not a hard core vibe coder i know vibe coding but i dont do code like slopocolypse sort of coding i prefer doing a planning, brain stromming, documenting and implementing sort of person so yeah there is lot running but this is for the moment …

Can someone give a referral for a job at HP? Will dm u the job link

We are a small technical team focused on GPU kernels and production inference systems. We help AI startups, inference API teams, GPU providers, and companies running self-hosted models make inference faster, more reliable, and less expensive.

If your team is responding to slow generation or high GPU bills by adding more hardware, we can first determine whether the real constraint is kernels, memory movement, quantization, KV-cache layout, batching, scheduling, model loading, or the serving architecture.

We can help with:

• CUDA and Triton kernel development, fusion, profiling, and benchmarking
• PyTorch model-path optimization and numerical validation
• vLLM, SGLang, TensorRT-LLM, and custom serving systems
• quantization, VRAM reduction, KV-cache layout, continuous batching, and CUDA Graphs
• time to first token, inter-token latency, throughput, and tail-latency analysis
• scale-to-zero, cold-start behavior, readiness, cancellation, streaming, and failure recovery
• GPU utilization, cost-per-request analysis, and capacity planning
• reproducible before/after benchmarks with correctness gates and documented limitations

A typical engagement starts with a focused inference audit:

1. Define the model, framework, GPU, traffic shape, latency or throughput target, and cost constraint.
2. Profile the current path and identify the dominant bottlenecks.
3. Agree on the smallest changes worth implementing.
4. Deliver code, before/after measurements, correctness checks, and remaining risks.
5. Help integrate and validate the optimized path under realistic load.

Good fits include a slow or expensive inference endpoint, VRAM pressure preventing the desired batch size or context length, unstable serving under load, poor GPU utilization, costly cold starts, or a custom kernel/model path that needs profiling and validation.

For an initial conversation, send a Reddit chat or private message with:

• model and framework
• GPU type and VRAM
• current latency, throughput, utilization, or cost problem
• target workload and success metric
• deployment environment
• timeline and approximate budget range

We will tell you directly whether the workload appears optimizable and what evidence would be needed before making a performance or savings claim.

Hi everyone!

I’m exploring Data Engineer opportunities with 3+ years of experience in PySpark, SQL, Spark, and AWS.
If your company is hiring, I’d appreciate a referral.

Happy to share my resume via DM.

Hello,

I am data engineer with 6+ years of experience in this area. I have experience in designing and building scalable data platforms across AWS, Azure, GCP, and modern data lakehouse architectures. Skilled in both batch and real-time data processing, data modeling, and building data products for analytics and business reporting. Experienced in working with distributed teams, stakeholders, and implementing end-to-end data solutions including governance, CI/CD, and infrastructure as code.

I am looking for something additional, I am open for any kind of ideas, offers:)

Hi everyone.

I’m currently looking for my first full-time position as a Data Engineer, and I’d appreciate some honest feedback on my CV.

I have an engineering degree specializing in Data Engineering and AI, with experience working on data pipelines, data ingestion and transformation, Python microservices, data lakes, and RAG systems. I’ve also built a personal end-to-end batch and streaming data platform using Kafka, Spark, Airflow, PostgreSQL, and Grafana.

I’ve been applying for jobs, but I’m not getting as many responses as I expected, so I’d like to understand whether my CV might be part of the problem.

I’d really appreciate your feedback on a few points:

• Is my CV clear and well-structured?
• Does it position me well for junior Data Engineer roles?
• Are my professional experiences described effectively, or should I rewrite some bullet points?
• Are there any skills or technologies I should highlight more or remove?
• Is there anything that looks weak, unclear, exaggerated, or irrelevant?
• What would you change to make my CV more attractive to recruiters?

I’m looking for constructive criticism, so please feel free to be direct.

Thanks in advance to anyone willing to take the time to review it.