WHAT YOU GET TO DO:

Own and evolve core systems for freight brokerage operations and related services: quoting, booking, pricing and rate logic, contracts, carrier compliance, facility matching and location/address data quality, and scheduling-oriented workflows.

Lead technical initiatives across several Python/Django services, including design, phased rollout, and safe deprecation—not only single-service features.

Drive platform and reliability work: database and dependency upgrades, search/indexing changes, performance tuning (queries, indexing, caching), secrets/credential patterns, and tooling that prevents bad deployments (e.g. migration safety, validation pipelines).

Ship high-impact product and revenue work: pricing and benchmark integrations, compliance/load-registration flows, feature-flag and segmentation strategies, and scope-shaping that ties technical design to business outcomes.

Define engineering standards for the squad and beyond: ADRs, shared client/integration layers, test and observability patterns, and code review culture that teaches (SOLID, architecture, thorough PRs)—including shared patterns between API and UI.

Operate production systems: incident response, war-room coordination, root-cause analysis, and fast stabilization PRs when freight brokerage operations are at risk.

Collaborate tightly with product on discovery and requirements—framing problems, narrowing scope with technical options, and sometimes presenting outcomes to senior stakeholders.

Mentor and level up teammates through pairing, design review, and informal leadership—without necessarily being a people manager.

REQUIRED QUALIFICATIONS:

Senior back-end engineer with a track record of owning ambiguous, multi-step projects end-to-end across API and UI (design → implementation → rollout → observability), comparable to tech-lead-level delivery on a business-critical system.

Strong Python and Django (or demonstrably deep transfer: e.g. long professional experience in Java/JVM ecosystems plus substantial Python web work in production).

PostgreSQL expertise: schema design, production database migrations, query plans, indexing, and fixing latency/regression issues—not only CRUD.

Distributed systems and multi-service reality: async jobs/queues, events, idempotency, integration failures, and debugging across service boundaries.

Proven impact outside “greenfield” only: experience in large or evolving codebases, modernization (framework/DB/search/runtime upgrades), or cost/performance work with measurable outcomes.

Production mindset: on-call or equivalent experience, incident response, and prioritizing stability under operational load.

Technical leadership without relying on title: examples of leading migrations, integrations, or architecture decisions; mentoring; and aligning engineers across teams.

Clear written and verbal communication: design docs, ADRs, RFCs, or review threads that other engineers adopt as reference.