Let's get straight to what matters: this is a full-time, fully remote Snowflake Data Engineer role paying $89,500 a year, and you can do it from wherever you already live and work.
A quick note before the details. This posting is for engineers who already know Snowflake, not for people hoping to learn it on the job. If that is you, the fit and the pay should both make sense fast.
If you have spent real time inside Snowflake, tuning warehouses or untangling a slow query at 4pm on a deadline, you already understand more about this job than a bullet list can capture.
You will design, build, and test the software components that keep Snowflake-based data work reliable, usually alongside a small cross-functional team rather than in isolation. Say a reporting dashboard starts returning stale numbers because a transformation job silently failed overnight. You are the one tracing it back through the pipeline, fixing the root cause, and writing down what happened so it does not repeat itself next quarter.
Code review is treated as a real part of the job, not a rubber stamp before merging. Expect to spend a meaningful chunk of most weeks reading a teammate's pull request closely enough to catch a subtle issue, like a transformation step that silently drops null values instead of flagging them, before it reaches production data.
Version control habits matter more than people expect going in. Small, well-described commits make it far easier for a teammate in another time zone to understand what changed and why, without needing to catch you live to ask.
You will need a bachelor's degree in computer science, software engineering, or something adjacent, plus about two years of hands-on Snowflake experience. A portfolio helps here more than a polished resume does. Employers hiring for this role want to see pipelines you have actually shipped, not just a list of tools you touched once in a workshop.
Beyond Snowflake itself, comfort with modern development tooling matters: think CI pipelines, containerized environments, and whatever orchestration tool your future team has standardized on. You do not need to be an expert in all of it on day one, but you should be quick to pick up a new tool without much hand-holding.
Teams hiring through Remoteroles for roles like this tend to value async communication, since remote and worldwide means your teammates might be six or ten time zones away on any given day. Documentation becomes the connective tissue that holds the team together when live meetings are hard to schedule, which is part of why maintaining it well counts as real engineering work here, not an afterthought.
There is also something satisfying about owning a system end to end. When you fix a pipeline issue and watch the downstream dashboard correct itself an hour later, you get a feedback loop that a lot of engineering work does not offer.
Hours are largely yours to arrange, as long as you overlap enough with the rest of the team to unblock each other during the workday. Plenty of engineers on distributed teams like this one end up working an early morning block, taking a long midday break, and finishing work in the evening. Others just work a normal day. Both are fine here.
Teams built around this kind of role tend to stay small on purpose, often just a handful of engineers alongside analysts and a product owner who depend on the pipelines staying reliable. That size means your individual work is visible. A shortcut taken under deadline pressure shows up in someone else's dashboard within a day or two, so the incentive to do things properly the first time is built into the structure rather than enforced from outside.
That stipend is worth calling out specifically. A second monitor or a better chair is a small cost for the company and a real quality-of-life improvement for someone doing detailed pipeline work eight hours a day.
Testing habits round out the picture. A pipeline that works today but breaks silently the moment an upstream schema changes is a common failure mode in this kind of work. Engineers who write a few solid checks against expected row counts or key fields tend to catch that failure within hours instead of leaving it for an analyst to discover a week later when a report looks wrong.
Engineers who spend a couple of years owning Snowflake pipelines end up with a genuinely marketable specialty, since demand for that particular skill set has stayed strong even as broader data engineering hiring has slowed in places. Some move toward data platform architecture from here. Others stay close to the pipeline work itself because they enjoy it, and that is a perfectly reasonable path too.
One more thing worth knowing: this role sits inside a company already committed to Snowflake as its core data platform, not somewhere still deciding between three vendors. That stability means less time spent on migration projects and more time spent actually improving the pipelines you own.
Send a resume along with a portfolio or links to any Snowflake-related projects you can share, even personal ones if your professional work is under NDA. If your background lines up, expect the first conversation to dig into a specific pipeline problem you have solved, since that tells an engineering manager more than a general skills discussion would.