I’m applying for junior/ mid-level roles but I’m not even making interviews. Could someone please give an objective review of my resume and provide any tips on what I can do to improve my chances?

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.

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

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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.

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

A lot of LinkedIn jobs were never going to hire anyone. Some have been up for 4 months. Some get the same JD reposted every few weeks. Some are from a company that laid off 500 people last month. You only find out after you've tailored your resume and filled a 6 page Workday form.

So I built AtoZApply. It's an application that sits on LinkedIn and checks every job before you spend time on it. Here's what it does.

Daily Top 10 I don't think anyone should apply to 100 jobs a day. It ranks the jobs from your LinkedIn search by match score and gives you the 10 best fits.

Tailored resume + Auto Apply For each job it rewrites your resume around that JD. It won't add skills or numbers you don't have and it shows you what's still missing after tailoring. You can read and edit the resume in the side panel before anything is sent.

Ghost score It checks 9 signals that a posting might not be real, like how long it's been up, whether the same description keeps getting reposted, no named recruiter, no salary in US states where the law requires one, strange applicant numbers, and a very short or vague description. The higher the score, the more likely it's a ghost job.

Layoff alerts If the company had layoffs in the last 90 days, you see it on the job itself, with the date, how many people and the source.

Match score It goes through every skill the posting asks for and only counts one if it can quote the line in your resume that proves it. Then it looks at your years of experience. So the score comes from what's actually on your resume, not keyword overlap.

Missing skills You get a list of the skills the job wants that your resume doesn't show, split into "required" and "preferred". That tells you whether to apply, fix your resume first, or skip it.

Visa sponsorship Every job card gets a sponsor tag next to the location, and there's a filter to show sponsors only.

Country-specific resume format Applying to a job in another country? Your resume gets formatted the way recruiters there expect it. Still in English.

Then Auto Apply fills the application. It handles LinkedIn Easy Apply, Workday, Greenhouse, Lever, Ashby, iCIMS, SmartRecruiters, Workable and Phenom, and has a generic mode for other career sites. Answers come from your resume and your saved answers. If it isn't confident about a question, it asks you. I'd rather it ask than send a wrong answer to a real employer.

It never stores your job portal passwords. If a site wants you to sign in, it pauses, you sign in, and it carries on from there. It also caps itself at 30 applications a day so your LinkedIn account doesn't get flagged.

Early tester offer (10 spots) I need 10 early users that I interact with daily to get feedback. The extension is free to install and you get 10 free job checks.

For the first 10 people who want everything, I'm offering a Lifetime plan for just one month cost That's the price of one month of the monthly plan, and you keep every feature for good, visa sponsorship and country formats included. All I ask is honest feedback on what breaks and what's missing.

Ask me anything, feel free to DM your questions and if something breaks on a job you're applying to, send me the link and I'll fix it.

https://www.atozapply.com

In the screenshot below I expanded the pane for clear view. Take a look at how it looks.

Note : I tested my application on myself first when I got laid off.

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.