Flatiron Health Data Analyst candidates describe a timed coding screen in SQL, R or pandas, followed by live technical conversations that mention BigQuery. One candidate also reports a final multi-session day with a product case.
$105K
Avg. Base Comp
$135K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Candidates who interviewed for Flatiron Health's Data Analyst role describe hands-on technical screens more than abstract theory. Three reports cover the role, and they don't describe the same path, so treat the details as individual experiences.
One candidate took a 1.5-hour HackerRank test split between SQL and pandas. The SQL question combined joins, case statements and coalesce, and the pandas portion asked for KPI calculations. That candidate says the automated grader left little room for small mistakes and that no score or feedback came back. Another candidate describes a 90-minute online challenge on basic R data manipulation, then a live coding session with the team manager covering SQL, database design and BigQuery. That candidate also mentions SQL, R, Python and AWS coming up across the rounds.
The third report describes three rounds: a Karat screen on statistics, SQL and R, a conversation with a Data Insight Engineer about R and a BigQuery case, and a final day with a behavioral question, a live dplyr pipeline exercise and a product case with a PM and the hiring manager. This candidate found the interviewers friendly and the first-round questions easy to moderate.
All three reported no offer. The common thread is that R or SQL fluency under time pressure seems to matter. Practice writing joins with conditional logic, dplyr transformations and pandas KPI calculations until you can do them quickly and accurately. Be ready to explain your reasoning aloud, since the live rounds were described as conversational.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Flatiron Health process.
I went through three rounds with Flatiron Health for a Data Analyst role. The first round was with Karat, a technical interviewing platform, and focused on statistics, SQL, and R. The interviewer was patient and thoughtful, which made the experience feel less intimidating than I'd expected for a Saturday afternoon session. Questions were at an easy to moderate level and covered foundational coding rather than anything obscure.
The second round was a conversation with a Data Insight Engineer where we dug deeper into R coding and discussed some case work involving BigQuery. This felt more conversational than the first round—less of a grind and more about understanding how I approach problems and communicate my thinking.
The final round was structured as a superday with multiple sessions back-to-back. There was a behavioral question to start, then a live R coding challenge focused on dplyr pipelines, and finally a product case session with a PM and the hiring manager. The dplyr question required writing a pipeline to transform and analyze data, which tested both my coding fluency and ability to think through data manipulation clearly. The case was a realistic scenario similar to what other tech companies ask. Throughout all three rounds, the interviewers were genuinely nice and didn't seem to be looking to trip me up—they wanted to see how I solved problems.
The whole process felt well-organized and the recruiters were transparent about timelines and what to expect. I appreciated that. Unfortunately, I didn't move forward, but the experience itself was solid and I learned a lot about where I need to strengthen my R skills, particularly in writing efficient dplyr workflows under time pressure.
Prep tip from this candidate
Focus on dplyr pipelines and SQL window functions in the context of real data transformation tasks—the live coding round emphasized practical data wrangling over algorithmic puzzles. Practice writing clean, readable R pipelines quickly, as that's where the technical bar seems to be.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial timed assessment of about 90 minutes. One describes a HackerRank test split between SQL (joins, case, coalesce) and pandas KPI calculations. Another describes basic R data manipulation. One candidate felt the automated grading left little room for small mistakes and received no score or feedback afterward.
Candidates report live technical conversations after the screen. One describes a Karat session on statistics, SQL and R. Another describes live coding with the team manager on SQL, database design and BigQuery. The tone was described as practical and conversational, with interviewers interested in how candidates reason through problems.
One candidate reports a second-round conversation with a Data Insight Engineer covering deeper R coding and a case involving BigQuery. Another candidate mentions Python and AWS coming up in discussion, so you may want to be ready to explain your past tooling experience in practical terms.
One candidate reports a final superday with several back-to-back sessions: a behavioral question, a live dplyr pipeline exercise, and a product case with a PM and the hiring manager. Not every candidate describes this stage, so the exact final format may vary, and you should confirm the schedule with your recruiter.
