Job Title: Staff Full-Stack Engineer
Reports To: VP of R&D
Location: Remote (United States)
Travel: Not required
About The Company
Our client is a research-and-technology organization that uses data, experimentation, and applied engineering to help partner organizations operate more effectively. They have run 100+ randomized controlled trials (RCTs) on behavioral data and supported hundreds of partner organizations in increasing their impact. The environment is fast-paced and adaptive — the team is comfortable shifting quickly as priorities evolve, and is looking for candidates who thrive in that kind of setting. The client's identity will be shared directly with qualified candidates as they progress through the process.
Position Overview
Our client is hiring a Staff Full-Stack Engineer to join their R&D team and help build the platforms that put data to work at the speed of thought.
The R&D team is where the client places its most ambitious technical bets — moonshots (high-upside projects with uncertain payoffs) that need the muscle to break through. Their flagship platform turns warehouse data into searchable, queryable large-scale databases, serves them through fast read-side APIs, and puts an LLM-powered agent in front of internal teams so they can analyze large operational datasets, build targeting segments, and design experiments. It's a Go backend (analytics, search, and geo services over DuckDB, SQLite, and AlloyDB), a React/TypeScript frontend with a type-driven visual grammar, an agent surface, and a CLI/SDK, all deployed on Google Cloud Platform and defined in Terraform. This person will work across all of it.
This is a role for an engineer who wants to own a product end-to-end: dbt model to API to UI to deploy, and back again. This role partners closely with the R&D lead, takes on projects that span the whole stack, and grows into owning systems that are already in use across the organization.
This role is fully remote and permanent.
Job Responsibilities
- Build and ship features across the platform end-to-end, from the data model and Go API through the React UI and the Terraform that deploys them
- Extend the read-side platform (the analytics, search, and geo services) and the caching, batching, and connection-pooling that keep them fast under load
- Work on the LLM agent surface and the visual grammar that renders its output as interactive, legible blocks
- Maintain and extend the CLI and Go SDK alongside the UI
- Build and operate services and infrastructure on GCP, and participate in on-call and rapid response, including evenings and weekends during high-stakes operational windows
- Load-test and harden systems ahead of key operational deadlines, when the platform serves hundreds of partner organizations on a non-negotiable calendar
- Self-review and validate your own work before requesting review; review teammates' code, mentor less-experienced engineers, and document systems so others can use them without spelunking through code
- Working systems, not code — code is a tool, not the job; the team builds systems that solve real problems for the people who use them
- Data is the foundation — most of the value created comes from how the data is organized underneath the system; the team invests in the dbt and read-side layers first
- Types are contracts — the team leans on a statically-typed backend and generates TypeScript from Go types so the two stay in sync, catching drift at compile time rather than in production
- Performance is a feature — fast read paths and fast feedback loops make everything downstream easier, including correctness
- Build it and run it — there's no separate ops team; infrastructure is code, and the team deploys what it writes and is on call for it, especially during high-stakes operational windows
- Small changes, shipped constantly — small, self-validated pull requests released to production several times a week, catching issues through monitoring rather than from a partner
- Reproducibility and simplicity — consequential operations are driven by immutable, declarative manifests, and the team does the simple thing first, adding complexity only when there's evidence it's needed
- 7+ years building and operating production software end-to-end, across both backend and frontend
- Strong proficiency in a statically-typed backend language (the team uses Go) and in modern frontend development with TypeScript and React
- Experience with the data layer: SQL, a transformation framework like dbt, and a cloud data warehouse (BigQuery or similar)
- Experience deploying and operating services on a cloud platform (the team uses GCP: Cloud Run, GKE, Cloud SQL/AlloyDB) and managing infrastructure as code with Terraform
- A track record of leading technical work that spans multiple systems, and of scoping ambiguous problems end-to-end
- Strong written communication: able to document what you build and explain complex systems to non-engineers
- Comfortable operating in a fast-paced, mission-driven environment with shifting priorities
- Experience building product surfaces on top of LLMs: agent workflows, tool calling, or structured generation
- Experience with high-performance read paths: columnar/analytical query engines (DuckDB), embedded databases (SQLite), connection pooling, and caching
- Experience in mission-driven, advocacy, or nonprofit sectors, including familiarity with the compliance and data-sensitivity considerations common to those environments
- Experience with large-scale behavioral, customer, or public-sector data
- Experience in on-call rotations and mentoring less-experienced engineers
