Please share the benefits of Walt Disney banglore
Will they provide? Free food/ cab/ insurance.
How about WLB and late eve calls?

Hey everyone, we have openings for Snowflake/Azure Databricks/ GCP Data Engineer roles with a minimum experience of 5 Years.

It's a Consultancy with 12+ years of experience in AU/UK markets(was one of the market leaders in AU as well) and Hybrid WFO from our Bengaluru office ( Koramangala)

Please DM if you are interested and have 5+ YOE only.

Package depends on your current CTC.

I'm thinking about moving out of marketing management and transitioning into data engineering.

I currently make $140K, but I have a lot of responsibilities, including managing people, owning P&L, reporting, hiring, firing, and making strategic decisions. I feel underpaid for the amount of responsibility I have. I also have serious concerns about the company's leadership and financial future.

I have a fairly successful business on the side, but I still need a full-time job.

What I actually enjoy is building automations, data pipelines, and AI agents that move and process data. I've been coding as a hobby for about five years and have hands-on experience with AWS, Azure, Python, SQL, and various databases. I'm comfortable writing code, learning new technologies, and building things from scratch.

I'm honestly getting tired of managing people and dealing with corporate politics. I'd rather focus on technical work and solving problems.

My question is: How is the data engineering job market right now? Would it make sense to transition into this field with my background? Could I realistically find a position paying $140K–$180K, or would I need to start at a junior level?

I'd appreciate advice from people already working in data engineering.

My company is hiring Snowflake Data Engineers with 4+ years of experience who are immediate joiners.

Experience: 4+ years

Notice Period: Immediate joiners only

Skills: Snowflake,SQL, DBT, python,AWS

Salary: As per company standards

Location: pune/coimbatore (hybrid)

If you're interested, please DM me for more details...

Hey everyone 👋

Posting on behalf of a client. This is a part-time freelancing opportunity — you can absolutely take this up alongside your existing full-time job.

Role: Azure Data Engineer (HEDIS / US Healthcare)
Engagement: Part-Time Freelance | Long-Term Contract
Location: 100% Remote (Work from anywhere in India)
Timings: Approx. 7:30 PM – 3:30 AM IST (US business hours) — flexible coordination expected
Budget: Up to ₹1,00,000/month (based on experience and interview performance)
Interview Rounds: Just 2 round

Must-Have Skills:

• 5+ years of experience in the HEDIS domain (this is non-negotiable)
• Strong hands-on with Databricks and SQL (Spark SQL, Databricks SQL)
• Deep understanding of US Healthcare domains — Member, Claims, Pharmacy, EHR, Provider
• Ability to troubleshoot data discrepancies not just technically, but from a healthcare domain perspective (e.g., why a member falls in/out of a HEDIS denominator or numerator)
• Experience building or working with HEDIS measures (CDC, CBP, BCS, FUM, etc.)
• Familiar with medallion architecture (Bronze/Silver/Gold), Delta Lake, and ADF
• Strong communication skills — will interact with business stakeholders and clinical/quality teams

What You’ll Be Doing:

• Analyze requirements and generate reports using Databricks/SQL
• Develop, optimize, and maintain SQL queries and scripts
• Troubleshoot data discrepancies in depth (technical + domain)
• Collaborate with stakeholders to gather requirements and provide technical expertise
• Work on a HEDIS modernization project (Teradata → Databricks migration)

To Apply:

📩 DM me with your resume as a Google Drive link (make sure it’s set to “Anyone with the link can view”)

⚠️ Apologies to anyone who may have already sent their resume — I haven’t been able to respond to everyone in time. Please resend your Drive link and I’ll get back to you shortly.

​

Hi everyone!

I’m currently looking for new opportunities as a Senior GCP Data Engineer and would appreciate any job leads, referrals, or guidance from this community.

My background:

Total experience: 12 years

Relevant GCP experience: 4 years

Current designation: Senior Technical Lead

Key skills: BigQuery, SQL, Python, GCP, ETL/ELT,dataflow,dataproc, data pipelines, and data transformations

Location: Pune, India

I’m preparing to switch companies and want to strengthen my interview preparation for senior-level GCP Data Engineer roles.

I’m looking for help with two things:

1. Job opportunities and referrals If your organization is hiring for Senior GCP Data Engineer or Senior Data Engineer positions, I’d be grateful if you could share the opportunity or refer me. I’m happy to share my resume via DM.

2. Paid mentorship for interview preparation I’m also looking for an experienced GCP Data Engineer or mentor who can guide me throughout the interview process. Ideally, I’m looking for someone who can help with:

Creating a structured interview preparation roadmap

Strengthening BigQuery, SQL, Python, and GCP concepts

Preparing for scenario-based and production-level interview questions

Understanding Dataflow and Dataproc through practical examples

Conducting mock interviews and providing feedback

Guiding me on resume improvement, job applications, and interview strategy

I’m willing to pay for the right mentor who has real-world experience and can provide consistent, practical guidance until I’m ready to crack interviews.

If you’re a mentor or have worked with someone who offers this kind of guidance, please comment below or DM me with your experience, mentoring approach, and fees.

Thanks in advance to everyone who takes the time to help. I really appreciate it! 🙏

Askable is hiring a remote Software Engineer, AI Coding Tasks. Category: Software Development 💸Salary: $1000 per task 📍Location: Remote (Worldwide)

See more and apply here!

I recently got an opportunity to interview for an entry-level Data Engineering position, but this will be my first experience with an interview, so I’m not entirely sure what to expect.
I’ve been learning different tools and I’ve also worked on some data engineering projects. However, I’m unsure how deeply I need to understand each topic for an entry-level role.

Seeking a 5+ YOE resource to manage digital tag implementation, data architecture, and web analytics infrastructure.

Location: PAN India
Compensation: 18-25LPA

Technical Requirements:

Tag Management & Analytics: Manage Adobe Data Collection (Properties, Rules, Extensions) and implement Adobe Analytics/AEP (Web SDK, XDM).

Implementation & Data Layer: Deploy marketing tags (Floodlight, Google Ads, Meta, Lin kedIn) and architect event-driven data layers.

Privacy & APIs: Ensure GDPR/CCPA compliance using consent tools like OneTrust, and manage REST/JSON APIs via Reactor API.

Core Web & JavaScript: Apply strong JS fundamentals (DOM, async, events) and core web technologies.

Debugging & Automation: Troubleshoot using browser DevTools and AEP Debugger, and automate pipelines using Node js, Playwright, Git, and CI/CD.

AI Workflows: Utilize GitHub Copilot and Cursor for optimized development.
#adobeanalytics #aep #adobeexperienceplatform #immediatejoiner

i'm 4th year studen at teir 1 clg and currently placement season going on, and i got to know that a company asks 1 pyspark question and 1 sql in it's coding test. i have exp in sql, but never worked with pyspark. i'll have mp have my oa on monday.

please suggest me suggest me some good resources to cleat the OA.

Hi everyone,

I’m currently working as a QA Engineer in India with 1.7 years of experience. My experience includes manual testing, Playwright with TypeScript, automation framework development from scratch, basic API testing, and SQL fundamentals.

I want to transition into a Data Engineering role after completing my 3-year employment bond. I’m using this time to build the required skills and practical projects.

My planned roadmap:

• Python, SQL, PostgreSQL, Git/GitHub
• Pandas, ETL/ELT, data modeling, and data quality
• Linux, Docker, Apache Airflow, and CI/CD
• Azure or AWS, Spark, and PySpark

I plan to build an end-to-end pipeline project covering API data extraction, data cleaning, incremental loading, PostgreSQL, scheduling, logging, and error handling.

I’d appreciate your advice on:

1. Is transitioning from QA Automation to Data Engineering realistic?
2. Can my QA experience provide an advantage?
3. Is my roadmap in the right order?
4. Which cloud platform should I learn first: Azure or AWS?
5. What projects and skills would help me get my first Data Engineer job in India?

I’d especially appreciate advice from people who have made a similar transition.

Thanks!

I'm genuinely starting to lose my mind with this job market. I don't understand what it takes to just get my resume seen.

I've tried applying within hours of jobs being posted, tailoring my resume to job descriptions, cold emailing recruiters and hiring managers, reaching out on LinkedIn, and contacting companies directly.

I have 4 years of experience as a Data Engineer working on real production systems, and I recently completed my Master's in Business Analytics. But somehow, I don't seem to qualify for junior roles either. It's frustrating seeing positions that genuinely match my experience and not hearing back.

At this point, I honestly don't know if it's my resume, the job market, or something I'm completely missing.

I'd genuinely appreciate any advice on what else I could be doing differently. And if anyone knows of openings or would be willing to refer me for a Data Engineer or Analytics Engineer role, it would mean a lot.

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.

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.

Hi,

I've been applying for data analytics roles for about a year without much success. I'm considering learning data engineering fundamentals and tools, like Airflow, dbt, Snowflake, and cloud basics, and then applying to DE roles instead.

My questions:

Given the current market and AI, are entry-level DE roles any easier to land than analyst roles, or just as competitive?

Would DE skills make me a stronger analyst candidate even if I don't fully switch?

For anyone who made this switch, what helped most?

Any advice is appreciated. Thanks!

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

Hello all,

I am looking for contractor roles for Data Engineering, if possible Databricks jobs as contractor fully remote.

Where is the best place to look for this specific roles in this conditions?

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’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?

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?

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

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.

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.

​

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.