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Salary: USD 170,000 - 190,000 / annual
Beacon Biosignals is transforming precision medicine for the brain through at-home EEG platforms for clinical development and diagnostics. The company combines EEG and cardiopulmonary signals to deliver reimbursable assessments for sleep and central nervous system disorders.
The Datastore team sits at the heart of Beacon's platform, defining the foundational data model and serving as the centralized repository and API for brain data collected from clinical studies. This Staff Software Engineer role emphasizes design and scaling of backend systems for data infrastructure: PostgreSQL data models, Kafka-driven event pipelines, and the GraphQL API that makes scientific and clinical data usable.
At this level, you will lead the design of complex systems within the Datastore rather than individual components, own the pipelines that deliver dataset snapshots into the warehouse, and collaborate across platform, application, and scientific teams to implement APIs that unlock new workflows. The role involves reasoning about schema design, indexes, and query behavior as a matter of course. Two key system properties shape the work: the GraphQL API is generated from the PostgreSQL schema using PostGraphile (so schema decisions are API decisions), and row-level security is enforced on effectively every table because clinical data from different studies and partners shares one schema.
Beacon is hiring two engineers into this role with different centers of gravity. One will focus on breadth: event pipelines, GraphQL API, core data models, and third-party integrations. The other will focus on depth in PostgreSQL (data modeling, performance tuning, operations) and supporting all engineers in developing schemas and access controls. Candidates should indicate which profile they are most drawn to.
The role is 100% remote from anywhere in the U.S., with office hubs in Boston, New York City, and Paris available for in-person collaboration.
**Requirements:**
- 7+ years of backend development experience, including at least 3 years focused on data platforms and backend infrastructure for data-centric applications (transactional systems, event pipelines, and APIs)
- Highly proficient in at least one technical area: data modeling, distributed systems, API design, or database engineering, with depth that others come to you for
- Strong proficiency in SQL with production experience working with PostgreSQL as an application database; fluent in writing and tuning queries, comfortable reasoning about schema design, indexes, and query plans
- Hands-on experience with data streaming and event processing using Kafka or equivalent (RabbitMQ, Pulsar, Amazon Kinesis)
- Proficiency in Julia or Python; Julia is a core Datastore language and a strong plus, but not required
- Experience deploying and operating services in containerized environments with familiarity in Kubernetes and Infrastructure as Code tools (Terraform and/or Helm)
- Track record of mentoring engineers and improving how a team works (practices, tooling, test coverage, documentation)
- Collaborative mindset and excellent written and verbal communication skills, especially in asynchronous and remote environments
- Self-directed approach with track record of thriving in hybrid or fully remote teams
- Experience with or interest in LLM-assisted or agentic coding tools in production with good judgment about guardrails
For the Datastore Systems profile: ability to lead design across platform breadth, proficiency in GraphQL and TypeScript/JavaScript (Node.js), experience designing event-driven pipelines end-to-end (producers, consumers, replay, idempotency), and leading complex system design across API, pipelines, and dependent services.
For the PostgreSQL & Data Modeling profile: deep PostgreSQL expertise (query planner, index selection, EXPLAIN ANALYZE), data modeling and schema design (entities, relationships, constraints, temporal modeling for clinical corrections), schema evolution under load (zero-downtime migrations, dual-write transitions, lock avoidance), PostgreSQL production operations (upgrades, replication, backup/restore, connection pooling, bloat and vacuum behavior, extensions), and ability to teach and document so the team understands the schema.
