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Jaroměř, Česko
Remote only
TeamSnap is seeking a Staff Software Engineer, Backend, to join our fully distributed engineering team and help us continue scaling systems that serve millions of daily users and tens of thousands of amateur sports organizations. Our stack includes Node, TypeScript, Elixir, Ruby/Rails, SQL, React, cloud-based services, event-driven systems, queues, background jobs, caching layers, and observability tooling.
We are looking for a hands-on Staff Backend Engineer who can build and improve APIs and services, reason through production issues across the system, lead technical design in ambiguous areas, mentor engineers, and raise the quality bar across the team. This person should be comfortable working closely with infrastructure, platform, and data teams without being expected to own those functions directly.
As an engineering team, we architect and build scalable systems using service-oriented and event-driven architecture. This role will help shape the technical foundation behind TeamSnap’s product experience, with a focus on backend systems, APIs, services, data performance, reliability, observability, and production quality.
Architect, build, and evolve backend APIs, services, event-driven workflows, queues, background jobs, data models, caching strategies, and integrations that support millions of users and thousands of sports organizations.
Own complex backend product and technical work from discovery through design, implementation, rollout, production support, and long term maintainability.
Lead ambiguous technical initiatives by breaking down problems, identifying risks, making practical tradeoffs, and helping teams move from uncertainty to clear execution.
Improve the reliability, scalability, performance, and operability of production systems through thoughtful architecture, strong testing practices, database optimization, caching, monitoring, alerting, and incident follow-up.
Debug complex production issues by reasoning across application code, databases, caches, queues, jobs, observability data, and the broader request lifecycle.
Partner with Product, Design, Engineering, Infrastructure, Platform, Data, Support, and other teams to turn roadmap needs into durable backend systems and practical delivery plans.
Strong interpersonal skills with the ability to give and receive constructive feedback, mentor engineers, influence cross-team alignment and articulate technical trade-offs to non-technical stakeholders
Staff level backend engineering depth, with a strong track record building and operating high-scale APIs, services, distributed systems, or shared product capabilities in production.
Strong command of backend technologies such as Node, TypeScript, Elixir, Ruby/Rails, Java, Go, Python, or similar, including runtime behavior, failure modes, and production tradeoffs.
Strong SQL and relational database judgment, including indexing, query optimization, migrations, profiling, data modeling, transactions, read and write patterns, and performance tuning.
Practical understanding of service-oriented architecture, event-driven architecture, queues, background jobs, webhooks, Redis or similar caching tools, and systems that need to scale under real production load.
Working knowledge of production infrastructure and platform concerns, enough to reason through deployments, containers, CI/CD pipelines, cloud services, networking basics, observability, and operational debugging.
Strong production ownership mindset, with the ability to use logs, metrics, traces, dashboards, alerts, incident reviews, and system behavior to improve reliability over time.
Demonstrated ability to lead through influence, mentor engineers, facilitate technical decisions, communicate tradeoffs clearly, and connect backend technical choices to customer impact, business needs, and long term maintainability.
Previous work in or close partnership with infrastructure, platform, SRE, DevOps, or data teams.
Familiarity with Elasticsearch, BigQuery, analytics pipelines, search infrastructure, reporting workflows, or high-volume data processing.
Responsible use of AI-assisted engineering workflows to improve debugging, testing, documentation, refactoring, prototyping, or developer experience without compromising quality or understanding.
