Senior Backend Software Engineer

Ketch•San Francisco, CA

15h•$160,000 - $220,000•Hybrid

About The Position

The AI era is breaking every assumption about data protection. Sensitive data moves faster, through more systems, with higher stakes than ever before. Traditional privacy and governance tools can't keep up â they put up a cookie banner and call it compliant. We go deeper: real-time enforcement of consumer privacy choices across every device, system, and third-party application where data lives. This is backend engineering where correctness matters, latency matters, and scale is real. You'll build the infrastructure that replaces the legacy tools failing enterprises today â and you'll see your work running live on sites used by millions of people. You'll build reliable, high-performance systems that make sense of messy, high-stakes data flowing through databases, pipelines, SaaS tools, websites, and mobile apps. What Makes This Different - You'll own real problems, not tickets. Engineers here take a customer problem and figure out how to solve it â you're not waiting for a product manager to hand you a spec. You think through the problem, define the approach, and build it. - You'll never be bored. You won't be on the same product for three years. You'll move across problems â maybe six months on one, then a new challenge, a new product, a new domain. There's always something new to learn and build. - Your code runs on real sites, for real users. This isn't internal tooling. You'll see your work live on major consumer-facing websites and apps, processing millions of decisions daily. - AI assistants are part of the workflow. We use Cursor, Claude Code, Copilot, and similar tools daily. We expect engineers to leverage them â and we evaluate candidates using them during interviews. - The company is at an inflection point. We're well-positioned to take over this market. The equity upside is real, the customer list is growing fast, and the engineering team has a direct line to the company's success. What We're Looking For We're hiring at both mid-level and senior levels â what matters most is that you can think through complex problems independently and ship production systems that work at scale. What separates great candidates from good ones: Can you take a complex, ambiguous problem, break it down, ask the right questions, and figure out what needs to be built? That's the skill we're hiring for. Language and framework experience matters less than your ability to think. The engineering team is ~15 people, entirely co-located at our San Francisco headquarters. This in-person collaboration drives fast iteration, direct customer interaction, and the kind of hallway conversations that make startups move. You'd report to VP of Product & Engineering. This is a hybrid role â in-office 3 days a week at our SF HQ (SoMa)

Requirements

Strong backend engineering foundation in Go or similar systems languages
Experience building APIs, distributed systems, or microservices in cloud-native environments (AWS/GCP/Azure)
Familiarity with databases and storage systems: Postgres, DynamoDB, ScyllaDB, Redis, Elasticsearch, or similar
Understanding of distributed-systems fundamentals: concurrency, queues/streams, transactions, event-driven patterns
Experience with gRPC, REST, and service-to-service communication
Comfort with Kubernetes, containers, and operational concerns (security, networking, resource management)
Interest in modern AI/data tools (LLM APIs, embeddings, retrieval)
Experience using AI coding assistants (Cursor, Claude Code, Copilot, or similar) in your daily workflow
Curiosity, grit, and a drive to figure things out without heavy direction â startup experience is a plus
CS degree or equivalent practical experience building production systems

Responsibilities

Design and build Go microservices that process distributed data flows and support real-time governance decisions across an organization's entire stack
Develop clean, well-versioned APIs that orchestrate permissions, data directives, and automated actions across external systems
Build distributed pipelines and event-driven architectures that transform structured and unstructured data and feed downstream intelligence
Own the full lifecycle â from understanding the customer problem through design, implementation, deployment, monitoring, and iteration
Apply resilience patterns (timeouts, retries, circuit breakers) to keep systems predictable under real-world load
Drive performance through concurrency tuning and efficient resource usage
Contribute to architecture decisions: service boundaries, data models, queue/stream strategy, and AI integration points
Talk directly to customers to understand their pain points and translate requirements into scalable system designs