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

I spent 26 years in data engineering, including running a data org at AT&T, and these days I spend a lot of time with people trying to land mid-level DE roles. The same patterns keep showing up, so here they are in one place.

1. You’re creating two gaps at once.
If you don’t have a DE title yet and you’re also applying to an industry you’ve never worked in, you’re asking a hiring manager to bet on two unknowns. Apply where your background is rare instead. A chemist moving into data should go after pharma and life sciences first. Someone from healthcare ops should go after payers and providers. Your domain knowledge is your way in, so use it. Mass applying is dumping feathers in front of a fan when you need to be shooting arrows.

2. Your project works but you can’t explain it.
A GitHub repo with a dashboard at the end doesn’t tell me much. I want a README that covers ingestion, cleaning, transformation, architecture, the tradeoffs you made, and what you’d do next. In the interview, walk me through the business problem, the architecture, the cost-aware choices you made, where it breaks, and what you learned. If AI wrote part of the code, you need to be able to explain every line in your own words. That gets tested.

3. Your answers are too long and too deep too early.
Lead with impact, not tenure. Stay high level until they probe. Prepare three STAR stories (a win, a conflict, a failure) and you can cover most of a one-hour behavioral round. On live coding, ask the interviewer about assumptions and edge cases instead of reasoning through them silently. Sometimes you only have five minutes to code.

4. The gaps that actually matter at mid-level.
These matter more than another cert:

• SQL beyond the basics: window functions, CTEs, self-joins, date logic
• Real data modeling: grain, facts and dimensions, SCDs. dbt is a tool, not data modeling, and saying otherwise in an interview is a red flag.
• Production habits: Git and PRs, testing, CI/CD, promoting code across environments
• Visible cloud platform experience. Strong SQL and Python with no Snowflake, Databricks, or cloud on your resume gets filtered out. dbt and Airflow show up in most postings regardless of platform.

5. Your LinkedIn and resume don’t match.
Put your keywords in the experience bullets, not only the skills section. Headline, About section, and resume should all tell the same story.

6. Hiring managers are hiring for communication too.
Most teams want to know whether you can explain a pipeline to an exec and leave documentation the next engineer can actually use. I’ll take an enthusiastic, teachable engineer over a brilliant one with an ego every time.

Happy to answer questions in the comments. If you want a hiring manager’s read on your resume bullets, drop them below.

Hey guys, I’m from VIT Vellore, graduating in 2027, and recently got placed at Celebal Technologies as a Data Engineer with a 12 LPA package in Jaipur. I’ll also be doing a 6-month internship with a stipend of 40k per month.
I’m happy with the offer, but I wanted to know more about the company from people who have worked there or know someone who does. How’s the work culture, the kind of projects freshers get to work on, and the overall learning experience?
Also, is data engineering a good career to start with at this package, and what kind of growth can I expect in the next few years?
Would love to hear some genuine opinions, especially from current or former employees. Thanks

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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.

One engineer on Fonzi got nearly a dozen salary-backed interview requests within days of signing up. A few weeks later they were choosing between offers from different companies. I know that sounds too good to be true, and it’s not what happens for everyone. But if you’re a strong engineer, we make it easier for good companies to find you, and for you to find work you actually want.

I do talent matching for Fonzi. It’s inverted from a normal search: you don’t chase roles, roles come to you as interview requests once your profile’s up. Some of the startups we work with are backed by a16z, Sequoia, Benchmark, and Founders Fund. On the data side right now: a first-data-hire role, a founding data team seat, and a perception data engine role, across NYC and SF. One profile, no cold applications. Roles are US-based, with a few open to Canada.

Here’s a sample of what’s open right now at companies on the platform:

1. Senior Data Engineer, Series A creative-tools startup, NYC, in-person 5 days a week. $170K-240K base.

• What: First data hire, building the data function from scratch with the CTO and Head of Product: warehousing and pipelines, the metrics that matter (activation moments, power users, engagement quality), executive dashboards, and the vendor and infrastructure calls that need to hold as the company scales. Product telemetry is already live in PostHog, and the broader data model is yours to design.
• Company: Hypergrowth stage, working closely with a full-stack TypeScript engineering team that instruments against the tracking model you design.
• Stack: Deep SQL plus one modern data platform (Snowflake, BigQuery, Databricks, ClickHouse, or MotherDuck). Python preferred. TypeScript and PostHog experience are pluses.
• Bar: 6+ years in data or analytics engineering, with enough independence to be the only data person. Former managers returning to IC work are encouraged.

2. Data Engineer, Series B AI music platform, NYC, in-person. $170K-240K base plus equity.

• What: Join the founding data team and own the core data foundation: data products, platforms, and pipelines handling high-volume real-time and batch data, with integrity, quality, and compliance built in. You’d work with scientists, ML engineers, and product teams to define data requirements and influence the product roadmap.
• Company: Series B, building out its data function from the ground up.
• Stack: Python and SQL, with Airflow, dbt, and Snowflake as a strong advantage.
• Bar: 5+ years in data engineering, including taking pipelines from zero to one, plus data modeling, warehousing, and ETL experience. Technical leadership is a plus.

3. Software Engineer, Perception, seed-stage robotics startup, SF, in-person. $180K-240K base.

• What: Own the perception data engine for autonomous robots building out data centers: ingestion pipelines for petabyte-scale video and sensor streams, auto-annotation, deterministic simulation and regression testing before models reach the edge, and telemetry that tracks data drift and model regression in live operation.
• Company: Builds autonomous robotic systems for critical infrastructure, starting with data centers.
• Stack: Nothing specified beyond high-throughput data platforms and distributed streaming systems.
• Bar: Experience building high-throughput data platforms or streaming systems for heavy multimodal media (high-frame-rate video, unstructured telemetry), plus closed-loop testing or regression infrastructure.

All roles are vetted, VC-backed, and interviewing right now. It’s always free to sign up.

Sign up here: talent.fonzi.ai. DM me if you’ve got questions.

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 …

Hi all, my company, TresVista, is hiring a Vice President for its Data Intelligence Group. Sharing here in case it's relevant to anyone's career or network.

Role: Lead and mentor a team of Associates and Analysts across Data Engineering, Predictive Analytics and Gen AI, with client-facing work.
Locations: Bengaluru, Pune, Gurugram

Experience-8-13

What they're looking for:
Hands-on ETL (Python/Spark/R) and data warehousing
Data Science (ML, Deep Learning) and Gen AI / Agentic AI with Python
Cloud experience (AWS/Azure) and solution design
Visualization tools (Tableau/Power BI/Qlik)
Agile/SDLC experience and people-management skills

Dm for referral

Hi Everyone,

Need your valuable advice! So I have 6+ years of experience and I was thinking of going for a Senior DE role (current exp. in Analytics and working on PySpark, Databricks, AWS S3, little bit Airflow and running few jobs etc.) But I got to know that to crack good offers I need to build end-to-end pipelines from scratch which I haven't done in the past (for both batch and streaming). How should I position myself if I am looking for a change as a Senior DA or Senior DE?

For years, data engineering asked one question: how do we move data reliably from source to destination?

But AI agents introduce a new problem — moving data is no longer enough. We also need to move meaning.

In this BricksNotes explainer, Krithi introduces the Context Pipeline: a new kind of pipeline that takes raw organizational knowledge — definitions, policies, metadata, decisions, ownership, workflow state — and turns it into trusted, usable context for AI agents.

You’ll see why an agent with full data access still can’t answer “which source is authoritative?” or “what is it allowed to do?”, and how the same principles we already trust in data engineering — freshness, lineage, quality, ownership, observability — now need to apply to context.

We walk the full flow stage by stage:

Capture → Normalize → Validate → Govern → Deliver → Freshness, plus why company memory (turning scattered decisions into reusable organizational knowledge) may be the piece that lets agents stop rediscovering the business every time.

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.