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Snout

Founding Data Engineer

Snout is hiring a Founding Data Engineer for a remote role open to applicants in United States. It pays $165k-$185k a year and Level rates it ; you can apply on Snout.

Level's read on this job

  • Works on AI ●●●○ is how Level rates this data engineer role: the daily work is on AI products, without building the model.
  • Pay on this posting starts 12% higher than the typical data engineer posting on Level.
  • This is listed as a remote role.
  • The posting mentions Postgres, dbt and data engineering among its tools and skills.
  • Snout has 11 other open roles on Level.

AI in this role

Build and scale the foundational data engineering infrastructure that powers internal reporting and LLM/AI tooling at a fast-growing veterinary wellness startup.

Postgresdbt
data-engineeringetlsqldatabase-modeling

🐶 Our mission


Every pet should get the care they need, regardless of cost. Vet med prices are up 40% since 2020, and pet owners are stuck having to pick between their bank account and furry friend. At Snout, we aim to solve this by enabling clinics to offer pet wellness plans, that actually work.

🚀 The opportunity


Snout is one of the fastest growing wellness plan providers in the veterinary space, trusted by clinics across the U.S. We’re a small but mighty startup team. We’re looking for a remote, U.S. based Founding Data Engineer to build the data foundation the rest of the company will run on.

💻 The role


This is Snout’s first dedicated data hire, reporting directly to the CTO. You’re inheriting a real, working system — a Postgres operational database and a dbt-modeled analytics warehouse that already powers internal reporting and our LLM/AI tooling — but one that’s grown faster than anyone’s had time to organize. Your job is to turn it into a firm, documented, trustworthy foundation.
That means first understanding what data we actually have and where it lives, then making it coherent. We ingest hundreds of millions of rows from multiple veterinary practice-management systems (Cornerstone, AllyDVM, and others) alongside our own application data — today the same concept can live in several places with different shapes, and part of your early work is reconciling those lineages into definitions people can trust. From there you’ll own and extend the warehouse, harden the pipelines and the nightly rebuild, and shape a model that makes analysis fast and reliable.
You’ll do the up-front work of a founding hire: talk to stakeholders across the company, understand what they need, present options and clear recommendations with their trade-offs and cost, and then execute on the path we choose. We’re hiring a data analyst after you — you’ll set them up to succeed and be the technical backbone they lean on. Do this well and you put Snout in a genuinely data-driven position and grow a team underneath you.
Snout is committed to building a diverse and inclusive team. We know that great candidates may not check every box — and that’s okay. If you're excited about this role and our mission, we encourage you to apply if you meet at least 75% of “what we’re looking for” including the first bullet point. If you need any accommodations during the application or interview process, please let us know — we’re happy to support you.


🛠 What you’ll do


  • Map Snout’s data landscape end to end — what exists, where it lives, what’s canonical vs. stale — and document it so it’s no longer tribal knowledge
  • Reconcile data from multiple source systems (application data + multiple PIMS ingests) into conformed, trustworthy models where a term like “appointment” or “invoice” means one thing
  • Own and extend our dbt warehouse: model the source-system activity that isn’t modeled yet, and raise the bar on tests, documentation, and data freshness
  • Own the ingestion pipelines and the warehouse rebuild — keep them reliable, and recommend where to simplify or consolidate
  • Keep clean, well-modeled data flowing to the tools that consume it, including our internal LLM/AI tooling
  • Run early discovery with stakeholders across GTM, CX, finance, and product; turn business questions into a build plan; present options with clear trade-offs and cost before executing
  • Set up and support the data analyst we hire next — be the technical foundation they build analysis on


💡What we're looking for


  • 6+ years building and maintaining production data systems as a data or analytics engineer
  • Deep SQL and hands-on PostgreSQL experience
  • Strong dbt and dimensional/data-modeling experience in production — you’ve built warehouses people actually trust
  • A track record of taming large, messy datasets from multiple source systems and turning them into something coherent
  • A pragmatist’s bias toward simplicity — you reach for the simplest thing that works, can justify every tool and layer you’d add, and can tell the difference between complexity that earns its keep and complexity that doesn’t
  • Strong communication: you can make a clear recommendation, back it with reasoning and cost, and commit wholeheartedly to a decision even when it isn’t the one you argued for
  • Comfortable being the first and only data person for a while — self-directed, and happy building the foundation before there’s a team

🌟 What will make you stand out


  • Experience as an early or first data hire who later grew a function or team
  • Python (or similar) for pipelines and tooling
  • Experience modeling data for LLM/AI consumption
  • Familiarity with veterinary or healthcare data, or PIMS systems (Cornerstone, AllyDVM, etc.)
  • Experience at a subscription or technology startup (Series A/B), on AWS


💖 Why you should join Snout


  • Fast growing, venture capital backed technology company focused on improving access to veterinary care
  • Ability to have a meaningful impact from Day 1, where your voice and opinion matters
  • Unlimited potential to grow in your career, learn and expand your skillsets
  • Collaborative, flexible, and friendly culture


❌ Why this role might not be a fit for you


  • You believe every problem needs a new tool, and you’d want to stand up a large modern-data-stack (many SaaS tools, heavy infrastructure) before proving it’s needed — we optimize hard for simplicity and cost
  • You’d rather be the analyst producing dashboards and insights than the engineer building the foundation those depend on
  • You need an established data team and mature infrastructure around you to be productive
  • A fast-moving startup culture feels overwhelming, or you’re uncomfortable with ambiguous, messy data from multiple source systems
  • You prefer working in a silo rather than collaborating across GTM, CX, finance, and engineering
  • You don’t live near an airport or have the flexibility to travel quarterly


💸 Compensation


  • $165 000 - $185 000 base salary
  • Equity

⚕️Benefits


  • Day 1 medical/dental/vision benefits
  • Flexible time off + 11 Snout calendar holidays
  • Paid parental leave

How we rate this

Founding Data Engineer at Snout rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data EngineeringETLSQLDatabase ModelingPostgresdbt

Also common in data engineer jobs at this level

Pay

This job starts at
$165k
Typical data engineer job
$148k

This posting starts 12% above the typical data engineer job. See data engineer jobs

Questions you could be asked

  1. Tell me about a project where data engineering was part of your work. What did you do?
  2. Tell me about a project where etl was part of your work. What did you do?
  3. Tell me about a project where sql was part of your work. What did you do?
  4. Tell me about a project where database modeling was part of your work. What did you do?
  5. Walk me through how you've used Postgres in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Data Engineering, ETL, SQL, Database Modeling, and Postgres. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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