Ibotta Leadership & Management

What’s a hallmark of good management here — and how is it reinforced weekly?

“You do not rise to the level of your goals. You fall to the level of your systems.” James Clear’s line from “Atomic Habits” has become a mantra on my team and it shapes how we structure our week.’

Every Monday, we meet as a full team and then again in smaller pods to review priorities, open questions and emerging themes. We start with a non-work question of the week, small, but it keeps us a team of people, not just nodes. From there, the rhythm does real work: it surfaces what needs support, what patterns are worth automating and where our approach needs to shift.

That system first mindset shows up in results, too. One analyst felt stuck scaling a dashboard overhaul; the ask was open ended and felt bigger than his reach. We built a system: identify patterns, define what stays versus goes, prove it out on his own work first. That clarity didn’t just unblock him, it raised the bar for how the whole team approaches insight quality.

That’s fitting for a team of analytics people. We’re naturally wired for systems and rhythms, but it’s the meaning behind the numbers that drives us. Those same systems are what turn that drive into real execution.

What keeps priorities obvious — and what signal shows it’s working?

Our JIRA timeline is the artifact everyone points to. It shows ownership by name, context (which product area and which “Boulder,” our term for an OKR objective, it ladders up to) and component, the system or request type it touches. It also surfaces status and most importantly for me as a people leader, what’s blocked or backlogged, so I know exactly where to step in and help unblock or reprioritize.

The signal that it’s working is momentum. We move from wide open, undefined questions to clear tasks and action items. When that velocity slows or actually stalls, that’s my cue to look closer.

Sometimes it’s simple operational troubleshooting, a data source or a piece of context someone’s missing. But when it’s not, that tells me a team member may need more support or a different resource, often another person on the team who’s solved something similar. I try to make that connection directly and specifically, so the person can work through it with backup rather than alone.

That combination, visibility into where work sits plus a habit of storming on it early, is what keeps priorities from quietly slipping.

What part of the strategy excites people — and what metric shows progress toward execution?

AI adoption is hitting our team in a real wave and what excites me isn’t the hype, it’s what happens in our hackathons when people apply a different lens to old problems.

One new analyst looked at years of experimentation on our direct to consumer app which tests features, onboarding experiences and app optimization. He noticed the metrics, pipelines and analysis steps were largely repeatable. In weeks, he built an AI experimentation hub that centralizes that corpus, makes past and present experiments searchable and surfaces the optimization and guardrail metrics for each. Work that used to take real digging now takes a fraction of the time. Next, we’re exploring how much of the analysis itself AI can support without sacrificing the integrity of the recommendations an analyst would normally own.

We’re never short on ideas. The metric I watch is adoption: does usage of tools like the AI experimentation hub rise, does time on repeatable tasks drop and does engagement from our product manager partners grow. We’ve seen that pattern with analyses and experiments that drove decisions. Now, we’re watching for the same signal with AI tools. Adoption and decisions are what tell us the idea was real.