My article on data quality hit 71,000 views in a week. I didn't expect that.
I've been writing technical content for years. Most articles get a few hundred views, maybe a thousand if I'm lucky. This one was different. It sparked 100+ upvotes on Reddit, generated 40+ discussions, and reached engineers at companies from early-stage startups to Fortune 500 banks.
What made this one work when others didn't? I spent the past week analyzing the engagement, reading every comment, and trying to understand what resonated. Here's what I learned about technical writing that actually gets read.
I Wrote About Pain, Not Solutions
Most technical articles start with a solution. "Here's how to implement data quality checks in Spark." "5 ways to optimize your pipeline." "A framework for..."
My article started with a problem: "Our data pipeline was dropping 10% of transactions and nobody noticed."
That opening line got more engagement than anything else I've written. Why? Because every data engineer has been there. We've all had that Monday morning panic when someone asks why the numbers look wrong.
Pain is universal. Solutions are specific.
When you start with pain, readers think "that's me." When you start with a solution, they think "does this apply to my situation?"
The most-upvoted comment on my Reddit post: "I felt this in my soul. Currently debugging a similar issue." Not "great solution" or "I'll try this." Just recognition of shared pain.
I Showed My Mistakes, Not My Expertise
I could have written "5 Best Practices for Data Quality" and listed industry-standard recommendations. Schema validation. Freshness checks. Data contracts. All correct. All boring.
Instead, I wrote about the time I deployed validation logic on a Friday afternoon and spent the next week recovering 10% of our transactions from raw storage.
The difference? Vulnerability.
When you show expertise, readers feel inadequate. When you show mistakes, they feel understood. The best technical writing doesn't make you look smart - it makes readers feel less alone in their struggles.
One engineer commented: "Thank you for being honest about this. Most articles make it seem like everyone has perfect pipelines except me."
That's the response that tells you your writing worked. Not "great tutorial" but "I thought I was the only one."
I Used Specific Numbers, Not Vague Examples
Generic: "Data quality issues can cause significant problems."
Specific: "We dropped 10% of transactions. Finance called. Then my manager called."
The specificity makes it real. Anyone can write about "quality issues causing problems." But 10%? That's a number someone will remember. That's a number that shows this actually happened.
Throughout the article, I used real numbers:
- 10% of data dropped
- Three weekends of being paged at 6 AM
- $100 becoming 10,000 (exactly 100x)
- Revenue reports 3-5% off
Each number grounds the story in reality. Readers commented on specific numbers more than anything else. "The $100 to 10,000 thing happened to us with currency conversion!"
Vague writing is forgettable. Specific writing is memorable.
I Wrote for Skimmers, Not Readers
Most people don't read articles. They skim them.
I structured every section the same way:
- Clear heading describing the mistake
- Opening hook - what went wrong
- The debugging journey
- The fix
- The lesson learned
Readers could skim the headings, pick the mistakes that sounded familiar, and dive into just those sections. Multiple people commented "I skipped to #3 because we just had a currency issue."
That's not a failure of writing. That's success. They found value without reading every word.
Good technical writing respects that people are busy. Structure helps them find what they need quickly.
I Ended with Questions, Not Conclusions
Most articles end with "In conclusion..." followed by a summary of what you just read.
I ended with: "What's your worst pipeline debugging story?"
That question generated half the comments. People shared their own disasters. Currency issues. Schema changes. Data quietly disappearing. Each comment added value for future readers.
The best technical articles start conversations, not just share information. Questions invite engagement. Conclusions end it.
I Published on a Platform Where My Audience Lives
I didn't just post on Medium and hope people found it. I posted directly to r/dataengineering on Reddit, where thousands of data engineers actively discuss their work.
Platform matters. A brilliant article posted to the wrong platform gets zero views. A decent article posted where your audience already hangs out gets thousands.
Where does your audience spend time? That's where you should publish first.
What Didn't Matter
Before the article went viral, I worried about things that turned out not to matter:
Perfect writing: My article has typos. A few sentences are awkwardly phrased. Nobody cared. The content mattered more than the polish.
Length: The article is long - over 2,000 words. Conventional wisdom says shorter is better. But if the content is valuable, people will read. Several comments said "this was long but worth every minute."
SEO optimization: I didn't optimize for search engines. I wrote for humans. The article ranks nowhere in Google but hit 71,000 views through Reddit and LinkedIn shares.
Fancy formatting: No graphics, no diagrams, no custom styling. Just text, code blocks, and clear headings. Content beats design.
Professional polish: I wrote how I talk. Contractions, sentence fragments, informal language. It feels more authentic than formal technical writing.
The Pattern Across Popular Technical Content
After analyzing my article's success, I looked at other high-engagement technical posts. A pattern emerged:
They all start with a specific problem the author actually experienced.
Not "here's a common issue" but "here's what happened to me last Tuesday."
They all show vulnerability.
Not "here's what I know" but "here's what I learned after screwing up."
They all use concrete details.
Numbers, names, specific error messages. Real things that happened to a real person.
They all invite discussion.
They end with questions or acknowledgment that others might have different experiences.
What This Means for Your Technical Writing
If you're writing technical content, here's what worked for me:
Start with a real problem you solved. Not a hypothetical scenario. Something that actually cost you time, caused stress, made you look bad.
Be specific. Use real numbers. Quote actual error messages. Describe the exact commands you ran.
Show the messy middle. Don't just show the solution. Show the three wrong paths you took first. The assumptions you made that were wrong. The obvious thing you missed for way too long.
Structure for skimmers. Clear headings. Predictable section structure. Make it easy to find the parts people care about.
Write for people, not search engines. Informal language. Short paragraphs. Conversational tone.
End with a question. Invite people to share their experiences. The comments add as much value as your article.
Post where your audience lives. Don't just publish and pray. Go to where engineers already discuss problems like yours.
The Unexpected Part
The article's success wasn't about the writing quality. It was about recognizing shared experience.
Every engineer who upvoted or commented had debugged similar issues. Weekend-only failures. Silent data loss. Schema changes nobody announced. They'd all been there.
The article worked because it said "you're not alone" better than it said "here's the answer."
That's probably the most important lesson about technical writing: your readers don't need you to be the smartest person in the room. They need you to be the honest one.
Write about what went wrong. Show your debugging process, including the dead ends. Use real numbers from real incidents. Be specific enough that people know this actually happened to you.
That vulnerability creates trust. And trust is what makes people read your writing, share it, and come back for more.
What I'm Writing Next
This experience changed how I think about technical writing. I'm planning more articles about real production incidents:
- The time our pipeline processed the same day's data 47 times
- When I accidentally made our entire data warehouse read-only
- The monitoring alert that cried wolf so often we turned it off (then regretted it)
Each one will follow the same pattern: real problem, specific details, messy debugging process, lessons learned.
Because apparently that's what 71,000 people want to read.
What's your experience with technical writing? Have you written content that unexpectedly resonated? Or struggled to get engagement despite great content? I'd love to hear what you've learned.
Connect with me on LinkedIn or check out my portfolio. Always happy to discuss technical writing, data engineering, or production war stories.
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Top comments (1)
Thanks for sharing!