Skill demonstrated
Every active member writes graded SQL, Python, or Spark on the platform. The audience is verified by what they have done here, not by self-reported tags.
For partners
Partner with the community where data engineers sharpen their craft
Partners show up inside the work itself: a challenge worth solving, a topic worth sponsoring, a role worth reading.
For hiring teams
Meet the community in its own house
Members solve problems in SQL, Python, Spark, architecture, and data modeling long before they take a meeting with your recruiter. A featured role sits beside that work, and the data engineers who are ready come to you.
For developer relations
Contribute, don't interrupt
The strongest partnerships add to the practice itself: a co-authored challenge built on your product's real problems becomes something the community works through, discusses, and remembers. That is what we scope with you.
5
Practice domains: SQL, Python, Spark, data modeling, pipeline design
100%
Of active members verified by graded code submissions, not self-reported tags
0
Member data shared with partners. Placement is by topic, never by person
1
Sponsor per challenge, always labeled as sponsored
The community
Who the community is
Working data engineers preparing for interviews: the audience skews senior and mid-level ICs, with the strongest concentration in SQL, Python, and Spark practice, and a meaningful staff-and-principal tail. Every active member has executed graded code on the platform, so composition reflects demonstrated skill rather than self-reported tags.
We deliberately do not publish audience numbers on this page: static figures go stale and invite false precision. Current aggregate composition, derived from graded submissions and refreshed quarterly, is shared with prospective partners during scoping. Aggregates only; member-level data is never part of any partner conversation.
Why partner with a community
What partnering with a community gets you
Earned attention
A placement here sits inside the work the engineer came to do. They are not scrolling; they are practicing. Your brand or your role lands at full attention.
In-market signal
Interview prep correlates with stack opinions and offer receptivity. You reach engineers who are choosing tools, choosing employers, and paying close attention to both.
Become a partner
Partnerships start as a conversation, not an insertion order
The partner program is young, on purpose: we open conversations as the community grows, and early partners work directly with the team. Tell us what you have in mind and we'll scope it with you.
Research and guides
How teams reach and hire data engineers.
Channel-by-channel research on hiring and marketing to data engineers in 2026. Updated monthly.
Hire
How to hire data engineers
Eight channels ranked. Cost-per-qualified-candidate, time-to-fill, and comp benchmarks.
Reach
How to market to data engineers
Twelve channels ranked by ROI. Six channels to skip. Six-month attribution framework.
Pillar
Developer marketing 2026
Channel taxonomy, budget benchmarks, and the DevRel-vs-marketing line.
Hire
How to hire senior data engineers
What separates senior from mid, where seniors actually look, and how to structure the loop.
Hire
How to hire staff data engineers
The platform-owner profile, the scarcity math, and the channels that reach them.
Audience
Where data engineers hang out online
The 20+ communities mapped. dbt Slack, MLOps Community, Latent Space, r/dataengineering, and more.