The Ideas That Made Data Engineers Impossible to Ignore in 2025
What worked, what didn’t, and what engineers actually responded to in 2025.
At the beginning of 2025, this publication had 3,543 subscribers, out of which only 21 were paid. Over the year, a lot of data engineers joined the community and stayed. We are finishing the year with 7,183 subscribers, 77 paid subscribers, and a total of 11,248 followers on Substack.
In 2025, I published over 90 pieces of long-form content, 92 to be precise, including articles and videos.
Looking back at what resonated, there are a few things worth highlighting, starting with the articles that attracted the most attention.
At the beginning of 2025, this publication had 3,543 subscribers, out of which only 21 were paid. Over the year, a lot of data engineers joined the community and stayed. We are finishing the year with 7,183 subscribers, 77 paid subscribers, and a total of 11,248 followers on Substack.
In 2025, I published over 90 pieces of long-form content, 92 to be precise, including articles and videos.
Looking back at what resonated, there are a few things worth highlighting, starting with the articles that attracted the most attention.
Top 10 Most Popular Articles
Five Red Flags of Mediocre Data Engineers
This article focuses on behaviors and mindset rather than technical skill. It highlights patterns that quietly limit engineers’ growth and credibility without them realizing it.
Getting a Salary Raise Because of Data Engineering Prioritization
Here I explain how prioritization is not just a planning exercise but a leverage tool. Engineers who control priorities tend to control outcomes, visibility, and compensation.
Data Professionals Are F*ing Delusional
This one challenges some uncomfortable beliefs many data professionals hold about impact, fairness, and how organizations actually work. The strong reaction showed how common these blind spots are.
Two Massive Database Mistakes
Although framed as database mistakes, this article is really about organizational and decision-making failures that surface through technical symptoms.
Stakeholders Don’t Care About Data Engineering
This piece reframes the relationship between engineers and stakeholders. It explains why technical excellence alone rarely earns attention or trust without clear business relevance.
Three Business Metrics Every Data Engineer Must Know
The goal here was not to turn engineers into product managers, but to help them understand the metrics that influence decisions so they can better position their work.
Testing ELT Pipelines With Great Expectations
This article connects data testing to trust and reliability rather than tooling alone, which is why it resonated beyond just a technical audience.
Data Engineering Migrations Are Hard
Migrations are rarely just technical projects. This article explains how they expose unclear ownership, weak prioritization, and organizational misalignment.
Mapping Data Engineering Work to Business Outcomes
Many engineers struggle to explain why their work matters. This article provides a way to connect day-to-day tasks to outcomes leaders actually care about.
The Downsides of Reactive Data Engineering Work
Reactive work feels productive, but over time it limits strategic impact. This article explores why constant firefighting hurts long-term credibility.
Playlists
Level-up Data Engineering
Beyond individual articles, we spent the year working on hands-on projects together through the Level-Up Data Engineering playlist. The goal was to build practical skills and portfolios that help data engineers move into senior roles by practicing real scenarios instead of just consuming content.
https://www.datagibberish.com/t/level-up-data-engineering
Workshops
We also started running live workshops every month. These are not traditional articles but live sessions where we work through problems together. I find these particularly fun and useful, and I plan to double down on these practical resources in 2026.
https://www.datagibberish.com/t/workshops
Other Playlists
Throughout the year, I added new content to other playlists as well, including AI for data engineers and planning and scoping data work like a data product manager. These topics are increasingly important as the role continues to expand beyond pure execution.
What Didn’t Work
Not everything worked as expected. I started group coaching calls for paid subscribers. While questions kept coming in, attendance dropped over time. We decided to pause this initiative, which gave me clarity on how I want these sessions to look in 2026.
https://www.datagibberish.com/t/group-enablement
Another initiative that didn’t quite land was The Profitable Data Engineer Framework. Some of you reported great results, and I even have screenshots to prove it, but the overall feedback was that it needed to be more practical. As a result, this will evolve into a new set of manuals in 2026.
https://www.datagibberish.com/t/the-profitable-data-engineer-framework
Courses
On top of everything else, I launched two courses this year.
SpaceX Pipeline
This is a completely free course designed to help beginners grasp data engineering fundamentals faster. Over 27 days, it walks through environment setup, cloud data warehousing, building a full pipeline with Python and dbt, and finishing with SQL reporting. I’m particularly proud of this one, and someone recently told me it played a key role in them landing their first job.
https://sendfox.com/ivanovyordan
The Data Leader’s Influence System
This is my premium offering and a complete system of templates, prompts, and scripts designed to help data professionals be seen as strategic leaders. It captures the mental models and practical approaches that helped me grow into a Head of Data Engineering role in a relatively short time.
I’m currently expanding it with more videos and a larger Notion vault planned for late January or early February.
Until the end of January, the course is available with a 25 percent discount, and all future updates are included. Instead of $97, it’s available for less than $73.
https://ivanovyordan.com/b/influence-system
Looking Forward
Data Gibberish wouldn’t be what it is today without a community of technical professionals who want to go further in their careers. In 2026, I’ll continue writing and recording content that puts you at the center, with at least two pieces of content per week, more live calls and streams, and more involvement from community members and guests.
The more you reach out through email, chat, DMs, or comments, the better this publication becomes. I’m excited for 2026 and curious to see where we take this next.
Thanks for reading,
Yordan
PS: That’s all for 2025. I wish you a happy and successful 2026. See you on the other side!