How to Market on r/dataengineering
A community for data engineers building data pipelines, warehouses, and platforms. Discussions on ETL, data modeling, orchestration, and the modern data stack. From Spark to dbt to Airflow and beyond.
r/dataengineering Rules & Self-Promotion Policy
Understanding the rules is critical for successful marketing. Here's what you need to know about r/dataengineering.
Moderate Self-Promotion Policy
Self-promotion is allowed in context. Lead with value, not your product. Promotional posts may be removed.
Community Rules
- 1No job postings or recruitment spam
- 2Include context in questions
- 3No low-effort content
- 4Be respectful and constructive
- 5Use descriptive titles
How to Write for r/dataengineering
Technical and practical. The community appreciates battle-tested insights from production systems. Share what worked, what didn't, and why. Tool opinions should be backed by experience.
Best Practices for r/dataengineering
Maximize your impact by understanding when, what, and how to post.
Best Times to Post
- Weekday Morning Est
- Tuesday Wednesday Est
- Thursday Afternoon Est
Posts stay relevant for about 24-48 hours
Content That Works
- Data stack architecture case studies
- Tool comparisons with production experience
- Open-source data tool announcements
- Pipeline optimization stories
Common Flairs
Who's Here
Data engineers, analytics engineers, and platform engineers working with the modern data stack. Many work with tools like Snowflake, Databricks, dbt, and Airflow. Value practical experience over theoretical discussion.
Common Mistakes on r/dataengineering
Avoid these pitfalls that get marketers banned or ignored.
Promoting tools without production context
Data engineers have seen too many tools that work in demos but fail at scale. They need real evidence.
Instead
Share production usage: "Running at [scale] for [duration]. Here's what we hit and how we solved it."
Ignoring operational concerns
Pipelines need to run reliably. Cool architecture that's hard to maintain isn't impressive.
Instead
Address operability: "Monitoring: [approach]. Alerting: [setup]. On-call burden: [assessment]."
Data stack absolutism
The modern data stack is fragmented. Claiming any one approach is universally best invites pushback.
Instead
Acknowledge context: "This works for [scale/use case]. For [different case], I'd consider [alternative]."
Vendor lock-in without acknowledgment
Data engineers worry about portability. Proprietary-only solutions need to justify the lock-in.
Instead
Address portability: "Uses Spark under the hood, so migrating is [realistic/approach]."
Focusing on flashy tech over boring reliability
The community values pipelines that just work. New tech is exciting but reliability matters more.
Instead
Lead with reliability: "Running 6 months with 99.9% uptime. The boring parts that made this work."
Post Formats That Work on r/dataengineering
These content formats consistently perform well in this community.
Stack Architecture
Example Format
""Our data stack: [diagram]. Scale: [data volume/users]. What works, what we'd change, and total cost of ownership.""
Why It Works
Concrete architecture. Real scale. Honest assessment. Cost context.
Tool Migration
Example Format
""Migrated from [old] to [new]. Timeline: [duration]. Challenges: [list]. Result: [improvements]. Would we do it again?""
Why It Works
Real migration experience. Honest about challenges. Retrospective value.
Open Source Announcement
Example Format
""Built [tool] to solve [data engineering problem]. How it works: [approach]. Compared to [alternatives]: [benchmarks]. Apache 2.0 licensed.""
Why It Works
Clear problem solved. Technical depth. Benchmarks. Open source.
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