About Takt

Takt is a warehouse intelligence platform that unifies labor, automation, and robotics data so 3PLs, retailers, and e-commerce companies can run more efficient operations. Warehouses generate enormous amounts of data and most of it never reaches the people doing the work. We build the software that closes that gap, giving supervisors and frontline employees the information they need, when they need it, on the floor where decisions actually get made. Our software runs every shift, every day, in real facilities for leading brands including Macy’s, Target, Stitch Fix, Flexport, ODW Logistics, and CarParts.com.

About the Role

We’re hiring a senior backend engineer to design and build the distributed systems at the core of the Takt platform. Warehouses produce large, fast-moving datasets from labor systems, automation, and robotics, and our backend turns that stream into the real-time operational views our customers run their shifts on. Our services are written in Go, Python, and Node.js and run on Google Kubernetes Engine across multiple regions. You’ll work across all three, picking the one that fits the problem.

This is a senior role with real architectural ownership. Our engineering team is small and works together to solve problems; you’ll work directly with our CTO and Director of Engineering, shape how the platform scales, and mentor other engineers as the team grows. Security is top of mind in everything we build: our customers trust us with operational data from their facilities, and we design for that trust from the first line of code.

What You’ll Do

Design, build, and operate scalable backend services in Go, Python, and Node.js that power the Takt platform.

Build integrations that bring in data from warehouse management, labor, automation, and robotics systems, and keep them reliable when upstream systems change or fail.

Deploy and run services on Google Kubernetes Engine across multi-region environments, designing for availability, failover, and data locality.

Model, store, and query data at scale with Google Cloud Spanner and Google BigQuery, working with large operational datasets from real warehouse systems.

Design APIs and data pipelines that serve real-time views to web, TV, kiosk, and mobile experiences on the floor.

Build with security top of mind: least-privilege access, safe data handling, and designs that hold up in SOC 2 and GDPR audits.

Own services end to end, from design and documentation through deployment, monitoring, and incident follow-through.

Mentor other engineers through design reviews, code review, and pairing as the team grows.

How We Build

We’re an AI-forward engineering team. Every engineer gets a monthly AI tools budget for Claude, Cursor, and whatever else makes you better, and we expect you to use it. AI-assisted development isn’t a shortcut here; it’s how we work. As a senior engineer, you’ll help set the standard for using these tools well: reviewing generated code critically, understanding what ships under your name, and building guardrails so the whole team can move fast safely.

What We’re Looking For

5+ years of professional backend engineering experience, or equivalent practical experience.

Deep, hands-on experience building production services in at least one of Go, Python, or Node.js (TypeScript), and the willingness to work across all three.

A track record of designing and operating scalable distributed systems, and reasoning clearly about consistency, latency, and failure modes.

Experience running services on Kubernetes in a cloud environment, ideally GKE on Google Cloud.

Strong SQL skills: writing, optimizing, and debugging queries against large production datasets is a must.

Experience with large datasets and the databases that serve them; Spanner and BigQuery experience is a strong plus, as is depth in comparable systems.

A security mindset applied by default: least privilege, safe handling of customer data, and pragmatic risk trade-offs.

Ownership and drive: you take ambiguity and run with it, communicate clearly, and leave systems better documented than you found them.