Kottayam, India, Oct. 5, 2026: Vision Board EdTech, a career-focused training platform for Azure Data Engineering and Generative AI, reports that about 90% of learners who attend its sessions consistently go on to receive a job offer. The company says its learners collect between five and ten offer letters every week; its highest packages reach 1.25 crore (INR equivalent) internationally and 70 LPA in India, and it now counts more than 10,000 students in data and AI careers.

With more than 10,000 students in data and AI careers and five to ten offer letters a week, the training platform shares the five-step path its learners follow, from first SQL query to first offer.

The company is sharing the roadmap its learners follow, for IT professionals weighing a move into Azure Data Engineering. Its finding: employers care less about which certification you hold than whether you understand how data moves through a system end to end.

"The question we get most often is which certification to start with, and honestly, that's not the one that matters. What gets people hired is being able to walk through the entire journey of a piece of data, from where it enters the system to how someone finally uses it."

-Devikrishna R, co-founder of Vision Board EdTech

It matters more now because companies are moving data operations to the cloud, and someone has to build the pipelines that make that data usable. That is why Azure Data Engineering has become a dependable entry point into cloud and AI work.

Getting there takes more than learning a few Azure services. It takes hands-on practice with real pipelines, and consistency: in Vision Board's own numbers, regular attendance is the habit that lines up with the 90% offer rate. Here is the path its learners follow.

Step 1: Build a Strong Foundation

Before touching the heavier Azure services, get solid on the fundamentals: SQL, databases, Python, ETL, data warehousing, and the basic mechanics of the cloud. Skipping this step is the most common reason people stall out later. You can follow along with a Databricks tutorial just fine, but if you don't understand why the pipeline is shaped the way it is, none of it sticks.

If you're new to cloud platforms altogether, an DP-900 certification course is a reasonable first move. It gives you the vocabulary and mental map before you're expected to build anything. If you already work with databases or write code regularly, you can probably skip ahead into Azure-specific tools sooner.

Either way, the certificate itself isn't the goal. It's understanding how data actually moves between systems and how the pieces fit together. The certificate is just proof you got there. This is also the stage where regular attendance starts to pay off, since each session builds on the last.

Step 2: Learn the Core Azure Data Engineering Tools

Once the fundamentals feel solid, it's time to get hands-on with the tools data engineers actually use day-to-day: Azure Data Factory, Azure Data Lake Storage, Azure Databricks, SQL, PySpark, and the broader Microsoft analytics ecosystem, including Synapse.

Azure Data Factory is where most people start, since it's built for orchestrating pipelines. Databricks tends to come next, doing the real work of processing and transforming large datasets once they're in motion.

This is also where a structured curriculum earns its keep. Vision Board's program, for example, is built for working IT professionals with two or more years of experience, and pairs these tools with hands-on projects rather than isolated tutorials, working through Data Factory, Databricks, Python, SQL, and PySpark as part of one connected workflow instead of a list of separate skills to memorize.

"Data Factory and Databricks aren't two separate topics in our program. They're two stages of the same pipeline. That's exactly how they come up in interviews, so that's how we teach them."

-Devikrishna R, co-founder of Vision Board EdTech

Step 3: Gain Experience Through Real-World Projects

Tutorials will only get you so far. What actually makes you job-ready is having built something

end-to-end, where you're the one deciding how data gets collected, transformed, stored, monitored, and handed off for reporting or whatever the business needs it for.

A few project types are worth having in your portfolio: a batch data pipeline, a lakehouse setup, incremental data processing, some data-quality checks, or a reporting layer built around a real business scenario. Any one of these, done properly, tells an interviewer more than a certificate does.

A structured curriculum helps here too, mainly because it forces you through that progression in order, from basic exercises to full production-style workflows, instead of leaving you to guess what to build next.

The results in Vision Board's Hall of Fame show where that progression can lead. Remya, from the 2025 Batch A, is now a Lead Data Engineer at Cancer Council Australia on a 1.25 crore package, the highest international package among its learners. Lakshmi, from Batch B, is a Data Engineer at Optus on 1 crore.

Step 4: Understand Certifications and Career Requirements

A certification alone won't get you hired, but paired with real project work, it tells an employer you've actually put in the time. If you're prepping for DP-700 (Microsoft Fabric Data Engineer Associate), which replaced DP-203 in 2025, the exam itself is less important than understanding why each service behaves the way it does, since interviews rarely stop at "did you pass?" Expect to be asked to explain design decisions, troubleshoot a broken pipeline on the spot, or walk through a project you've built.

Azure also moves fast. Microsoft Fabric and Generative AI tooling are both reshaping what this role looks like, and it's worth tracking both even before they show up on an exam.

Step 5: Prepare for the Job Market

Employers aren't hiring the best pipeline builder on paper; they're hiring the person who can talk about what they've built. A clear resume, a LinkedIn profile that actually reflects your work, well-documented projects, and the ability to explain your decisions out loud all carry real weight in the final stretch.

For interview prep specifically, run through SQL, Python, Azure Data Factory, Databricks, PySpark, data architecture, and troubleshooting scenarios until you can talk through them without notes, not just answer them correctly on a quiz.

This is the stretch where a structured program earns its cost. Vision Board pairs the technical training with resume, LinkedIn, and Naukri optimization and mock interviews, so the goal isn't just knowing the

material but being able to explain it convincingly under pressure. The company says this is where its weekly stream of five to ten offer letters comes from. Sushil Kumar Dadhich (Batch A, 2025) is now at General Mills on 70 LPA, the highest Indian package among its learners, while Snehil Sharma (Batch C) is at PepsiCo on 62 LPA and Purushotham (Batch B) is at Chevron on 50 LPA. Not every story starts high: one learner featured in a Vision Board video testimonial moved from 4 LPA to 1.5 Cr.

Build Your Azure Data Engineering Career Strategically

None of this happens overnight, and trying to learn everything at once usually backfires. A steady plan, consistent practice, a handful of real projects, and the patience to keep going will get you there faster than cramming ever will. That is consistent with what Vision Board reports about its own learners: the ones who attend regularly are the ones collecting the offers.

Start with the fundamentals. Get comfortable with the core tools. Build a few projects you can actually explain in an interview. Then put in the time on interview prep specifically, since that's the step most people rush.

Azure Data Engineering is a genuinely strong move for IT professionals looking to break into the Data and AI space right now, and going deeper into platforms like Databricks along the way is one of the more direct ways to strengthen that foundation as you go. For free guidance along the way, Vision Board also publishes a podcast, now at more than 160 episodes across its social media channels.

About Vision Board EdTech

Vision Board EdTech is a career-focused training platform, founded by Vimal Shaji and Devikrishna R, built around one idea: skills only count if you can use them on the job. It has more than 10,000 students in data and AI careers, and its alumni work at organizations including Microsoft, Amazon, Deloitte, and KPMG. Its programs combine structured training, hands-on practice, and direct career support, from resumes to interview prep, so what learners walk away with holds up in a real interview, not just on paper.

For Azure Data Engineering specifically, that means working through Azure Data Factory, SQL, Python, PySpark, and the broader data engineering workflow as one connected track. Learners who want to go further can build on that foundation with focused courses in Azure Synapse Analytics or Azure Databricks. The aim throughout is the same: a solid technical base, real projects to point to, and enough confidence to walk into interviews ready.

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