Seed Health Data Analyst interview typically runs 3 rounds: recruiter phone call, hiring manager, peer interview. It took about 1-2 weeks and included no trick questions or live coding.
$120K
Avg. Base Comp
$148K
Avg. Total Comp
3
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Seed Health cares less about flashy analytics and more about whether you can operate cleanly in a real, messy environment. The strongest signal in the process is hands-on fluency with SQL and dbt: the recruiter already probed for specifics there, and later conversations kept circling back to how you’ve actually used those tools in past work. We’ve seen that vague familiarity doesn’t carry much weight; they want concrete examples of how you built, maintained, or debugged pipelines and models.
A recurring theme is the company’s interest in how you handle ambiguity without creating noise. One candidate was pressed on messy data and conflicting stakeholder priorities, which tells us they’re looking for someone who can make good tradeoffs, explain them clearly, and keep projects moving when inputs aren’t perfect. That’s a very different bar from a purely technical screen: Seed Health seems to value judgment under imperfect conditions as much as technical correctness.
We also notice that the peer conversation went into a deeper technical dive, but not in a trick-question or live-coding way. That usually means the team is testing whether your experience is real, consistent, and transferable. If you can walk through a dbt project end to end, explain what broke, and show how you reasoned through the fix, you’ll be speaking the language they seem to trust most.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Seed Health process.
First round - standard recruiter phone call. He asked me logistics about my location, salary, and the expectations of the role. From there he probed for specifics about my experience in SQL and DBT.
Second round - hiring manager. Talking about projects on my resume and how I would deal with messy data/ conflicting stakeholder priorities.
Third round - Peer interview. Specific deep dive into technical experience.
No trick questions or live coding.
Questions asked: Recruiter - tell me about how you've used DBT
Hiring manager- describe a project where you used DBT, what would you do to balance conflicting stakeholder priorities? How would you go about debugging messy data
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Seed Health
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Closest SAT Scores | |
| Top Three Salaries | |
| Experiment Validity | |
| Employee Salaries (ETL Error) | |
| Prime to N | |
| Weighted Keys | |
| Top 3 Users | |
| Bagging vs Boosting | |
| Popular Actions | |
| Hurdles In Data Projects | |
| Encoding Categorical Features | |
| Network Experiment Design | |
| P-value to a Layman | |
| Delivery Estimate Model | |
| Declining Applicants | |
| Detecting ECG Tachycardia Runs | |
| Size of Joins | |
| Valid Anagram | |
| Random Forest Explanation | |
| Target Indices | |
| Sort Strings | |
| Find Duplicate Numbers in a List | |
| Success Measurement | |
| Customer Success vs. Free Trial | |
| Testing Price Increase | |
| Assumptions of Linear Regression | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Skewed Pricing | |
| Prime Numbers Identification |
Synthesized from candidate reports. Individual experiences may vary.
The first conversation is a standard recruiter call focused on logistics such as location, salary expectations, and role fit. The recruiter also probes your experience with SQL and dbt, including how you have used dbt in past work.
This round centers on your resume projects and how you would handle real-world data problems. Expect questions about a dbt project you have worked on, debugging messy data, and how you would balance conflicting stakeholder priorities.
A peer conducts a deeper technical dive into your experience. The discussion focuses on your hands-on technical background, with emphasis on SQL and dbt, rather than trick questions or live coding.
