# Sr. Software Engineer II - Tech Lead - Data Products at Zoro > United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Azoro%3A4719964006 (read 2026-11-04). ## Facts (observed) - Company: [Zoro](https://alderhq.app/companies/zoro) - Location text: Chicago, Illinois, United States; Chicago - Where it can be done from (inferred): United States (evidence: "Location: Chicago, Illinois, United States; Chicago") - Posted: 2026-10-07 (27 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://www.zoro.com/careers/jobs?gh_jid=4719964006 - Skills listed (inferred): Docker ## Pay and sponsorship - Posted pay: $116k–$193k per year (from the listing's pay field) - Market band for similar posted roles (Chicago, n 23): p25 $157k, median $194k, p75 $237k ## Description (the employer's text) 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 This is the senior technical role on a data product team, in a data organization that is becoming a software-engineering organization and deliberately reinventing how engineering work is performed with AI. You will set the technical direction for what the team builds: APIs, backend services, shared libraries, operational data stores, and data-processing systems; and you will build alongside the engineers doing it. This is an individual contributor role with no direct reports. Every data product team at Zoro runs on a three-way partnership: the product manager owns what gets built and in what order, the engineering manager owns the engineering practice and how engineers grow, and you own how it gets built, including the architecture, the interfaces, the sequencing of the work, and the call on whether a design is ready. You will be measured by what the team ships, not solely by what you ship yourself. The best tech leads spend their time on the decisions that are expensive to reverse and leave the rest to the engineers who will learn from making them. The role sits at the seam between software engineering and data science and asks for real strength in both: you may not be building models yourself, but you need to be credible with the people who do, and able to reason about whether an offline metric will hold up online, what a latency budget means for an inference path, and when a model is the right tool and when a rule would do. We expect Tech Leads to use AI in their own engineering and to shape how their teams use it. ### What You Will Do - Own the technical direction and architecture of your team's systems, break large problems into work the team can execute, and make the calls that are expensive to reverse. - Stay in the code every week, and use AI throughout design, planning, implementation, testing, and review. - Partner with your product manager on feasibility, sequencing, and tradeoffs, early enough to change the plan. - Partner with the Data Scientists and ML Engineers on your team, from problem framing through production serving, monitoring, and retraining. - Design interfaces, service boundaries, schemas, and data contracts that other teams can rely on, and negotiate them with those teams. - Lead design reviews and RFC discussions, and own the call on when a design is ready and on cross-cutting technical tradeoffs. - Grow the engineers around you through how you review, and partner with your engineering manager on where each one needs stretch or support. - Own your team's operational health: on-call quality, observability, and the systems you have in production. ### An Excellent Candidate Has - Experience as the Tech Lead on a team building production backend services, APIs, internal platforms, or distributed applications. - Experience designing APIs, data contracts, and operational databases that other teams consumed. - Familiarity with asynchronous processing, queues, event-driven systems, and change-data capture, including idempotency, retries, concurrency, and backward compatibility. - Working knowledge of machine learning and data science concepts: feature engineering, evaluation, training and serving skew, drift, and retraining; enough to review a model design and ask the questions that matter, and experience taking a model from notebook to production. - Experience making a team faster, through interfaces, tooling, or decomposition of work. - Comfort leading engineers who do not report to them, and partnering with a product manager whose priorities they do not set. - Familiarity with cloud infrastructure, containers, CI/CD, infrastructure as code, and observability. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Azoro%3A4719964006 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.