r/dataengineering
Data platform and pipeline engineers
- Size
- Mid-size
- Buying intent
- High
- Promotion
- Conditional
Concentrated purchasing power in a relatively small community. Data infrastructure is expensive, members discuss vendor pricing openly, and migrations are described in detail while they are happening, which is the ideal moment to be present.
The technical bar is high and the tolerance for marketing is low, but the community is notably generous with genuinely expert answers. A good contribution here is remembered because the population is small enough for that to happen.
What we have measured
Lidar reads this community every day. These figures come from the 33 posts we collected here over the last 30 days, read a median of 24 hours after they were posted.
- Posts asking a question
- 48%
- Median replies when we read them
- 4
- Median upvotes when we read them
- 5
The question share is the one to weigh. A community where half the posts ask something is a community that wants answers, and a community where almost none do is one where people are broadcasting rather than asking. The engagement figures are deliberately measured at the moment we collect a post rather than at its final score, because that is the state a thread is in when replying to it is still worth doing.
What people actually post
The migration in progress
Moving between warehouses, orchestrators or ingestion tools, with the reasoning stated as it happens.
The cost shock
A bill arrived and it was worse than expected. One of the most reliable triggers in this vertical.
The modelling debate
Architecture and methodology arguments. Good for credibility, rarely for pipeline.
What people get wrong about it
Its size suggests low value. Per member it has some of the largest software budgets on Reddit.
Can you promote in r/dataengineering?
Tolerant of disclosed participation from people who clearly do the work. Vendor accounts that only post about their own product are quickly identified and ignored.
What gets you removed
Any suggestion that the work can be automated away. You are addressing the people whose job you just described as unnecessary.
A good first contribution
Answer a question about a specific failure mode in a pipeline. The community is small enough that one good answer is noticed and remembered.
Products that fit here
- Ingestion, orchestration and transformation
- Warehouse cost optimisation
- Data quality and observability
- Catalog and lineage tooling
What does not work
Anything claiming to remove the need for data engineers. The framing will define you before the product is considered.
Timing
Cost-driven migrations cluster after quarterly cloud bill reviews.
How it compares to the neighbours
The communities closest to r/dataengineering, and how they differ on the three things that decide whether one is worth your time.
| Community | Size | Intent | Promotion |
|---|---|---|---|
| r/dataengineering | Mid-size | High | Conditional |
| r/devops | Large | High | Conditional |
| r/kubernetes | Mid-size | High | Conditional |
| r/Python | Very large | Medium | Conditional |
Where this fits in a wider plan
r/dataengineering appears in our list of the best subreddits for developer tools, which sets it against the other communities worth watching in that market. If you are starting from nothing, the six-week plan in finding your first 100 users covers how to build standing before you mention a product at all.
Watch r/dataengineering without reading it.
Lidar reads it every day, scores each post against what your product does, and sends you the ones worth answering. Along with the rest of engineering and infrastructure.