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How Cheers Health hired a Senior Analytics Engineer when nobody on the team had ever done it before
Cheers Health is a fast-growing consumer wellness brand selling across Shopify, Amazon, TikTok, and major retail. Their data infrastructure hadn’t been meaningfully updated in six years. It was fragile, undocumented, and creating bottlenecks that touched every part of the business.
Leadership knew they needed a Senior Analytics Engineer to own a full modernization of the environment. The challenge wasn’t finding one. It was knowing how to evaluate one.
Here’s how we helped them build the process to do it right.
The Challenge
Two separate problems made this hire high-stakes.
The data environment itself was in bad shape. Custom warehouse jobs ran that nobody fully understood. There was no documentation, no separation between raw, cleaned, and business-ready data, and business logic scattered across spreadsheets instead of the database where it belonged. One person was spending 4 or more hours a week copy-pasting data, with no way to delegate it. Every department depended on that one person for anything data-related. When something broke, the team found out from wrong numbers showing up in a report.
Getting this hire wrong wouldn’t just leave the problem unsolved. It would make it harder and more expensive to fix later.
The hiring challenge was just as hard.
A candidate who has built real data infrastructure and one who has just read about it can sound nearly identical in an unstructured conversation. The difference shows up in the follow-up questions, in the specifics of how someone talks about grain, migration risk, validation logic, and where business rules belong in the stack.
Without a process built for that depth, the hire goes to whoever interviewed most confidently. For a role that would set the architecture for everything that followed, that wasn’t a risk worth taking.
“Since me, Seth, and Leandro don’t have experience with this stuff, how would we evaluate this person? How would we know if they’re good or they’re just blowing smoke? I don’t really speak the language, or know what outstanding looks like. Is this something you could help us with?”
Brooks Powell – CEO at Cheers
The Solution
We built the full search and interview infrastructure for this hire, from role definition through to the offer conversation. Six deliverables, built in sequence.
👉 Role scoping and job architecture — translating the business problem into the right title, seniority level, and technical requirements before a single job ad went live.
👉 Targeted search and candidate pipeline — every candidate pre-qualified against the specific requirements before the Cheers team spent time on them.
👉 Three custom interview guides — one per interviewer, each with assigned competencies, owned questions, follow-up probes, and scoring rubrics covering a distinct dimension of the role.
👉 A “what good sounds like vs what a bluff sounds like” framework — for every question, what a 5 out of 5 answer actually looks like, what a confident but thin answer looks like, and what red flags to listen for.
👉 A universal tool reference for non-technical interviewers — covering every category a Senior Analytics Engineer should know and how to evaluate fluency in each.
👉 A technical case study built around Cheers’ real data environment — including sanitized sample data, a full business context brief, a grading rubric, and a follow-up question bank.
“This is AMAZING. Thank you for putting this together. I just went through it and it’s going to be so helpful.”
Brooks Powell – CEO at Cheers
The Results
Two finalists reached the technical case study stage. What happened with each one showed exactly why the structured process mattered.
The first finalist ghosted. He had confirmed receipt of the project, expressed enthusiasm, and went quiet without submitting. We flagged this immediately to the Cheers leadership team. A candidate who interviews confidently and can’t follow through on a clearly scoped task with a real deadline is showing you something important. The structured process created a clear, unmissable moment to see it before an offer was ever on the table.
The second finalist caught a genuine data modeling error embedded in the sample data. He flagged it, explained what it was, and documented why it mattered for the downstream architecture. That kind of signal only surfaces when someone is actually doing the work. It gave the Cheers team a concrete, data-based reason to move forward with confidence.
Offer extended. With the right person in the role, Cheers recovered 4+ hours a week in manual reporting and gave every department direct access to their own data for the first time.
The team now has a framework they can return to every time they’re evaluating a role where instinct alone isn’t enough.
Want to achieve the same results? Get in touch.
Get in touch via our website form and our team will get back to you to get started as soon as possible.
Don’t just take our word for it…
“Natan is a great culture fit and he seems to be the kind of person that will do what it takes to learn and succeed in this role over the long term.”
Seth Hazleton – COO at Cheers
