Company Summary

Zoro.com is a leading eCommerce platform offering nearly 12 million tools, parts and supplies for our business customers. Launched in 2011, we brought a B2C-like experience to the B2B industry, and continue to be at the forefront of digital innovation at the intersection of technology and distribution. We have rapidly grown to over $1 billion in annual revenue and we’re just getting started!

About the Role

Join a data engineering group that is evolving into a data focused software-engineering organization and is deliberately reinventing how engineering work is performed with AI.

This is an engineering management role in the data domain. You will lead the data and software engineers who build and interact with streams, services, shared libraries, analytical & operational data stores, and vendor integrations; it is a hands-on role. We expect our engineering managers to stay in the codebase every week and you will have the time and the support to do that.

Your engineers work inside data product teams, each of which are co-lead by a tech lead and a product manager who set direction and priority. Your focus is the engineers themselves: helping them make sound technical decisions, raising the quality of what they ship, supporting how they run it in production, and investing in how they grow. At this level you would support one to two data product teams' of engineers.

Because the work happens in teams you do not manage, much of your influence comes through partnership rather than reporting lines. You will not own the backlog, and you will not need to. The role is going well when a tech lead brings you into a design conversation early, when a product manager trusts your read on what is realistic, and when your engineers are making good calls without you in the room.

AI-assisted engineering is a core part of how we work, and we look for managers who practice it themselves rather than only encourage it. That means using AI to pressure-test designs, surface underspecified requirements, explore unfamiliar code, and to help your engineers build those habits into how they work, review, and learn.

What You Will Do

Manage, coach, and grow a team of software engineers embedded across one to two data product teams.
Partner with the tech lead and product manager on each team so your engineers are set up to make sound technical decisions.
Hold the engineering standard - design quality, interface and contract design, testing, observability, and operational readiness - without owning the product backlog.
Own delivery predictability, on-call health, and platform operations for your team.
Run hiring, performance, compensation, and calibration for your engineers.
Build per-engineer development plans against the team's technical roadmap, and close skill gaps before they become delivery risk.
Escalate and resolve priority conflicts when a product team's demands exceed what your engineers can sustainably deliver.
Drive adoption of AI-assisted engineering practices, and share what works across the wider data organization.
Participate in design reviews and technical tradeoff discussions, and stay close enough to the systems to have an informed opinion.
Represent your team's work and constraints clearly to stakeholders outside engineering.

An Excellent Candidate Has

Experience managing software engineers who build production backend services, APIs, internal platforms, or distributed applications.
Experience leading engineers who are embedded in product teams they do not report into, and a working model for making that partnership productive.
The technical depth to review a design, ask the question that was missed, and recognize when an approach will not survive contact with production.
Experience building a healthy on-call practice and treating operations as part of engineering.
A track record of growing engineers - promotions, scope increases, and people who got better on their watch.
Experience hiring, including calibrating a bar and closing candidates.
Familiarity with data systems - pipelines, analytical platforms, data modeling, event-driven architectures, or large-scale data movement - enough to lead engineers working in that domain.