Staff Software Engineer

Beacon Biosignals is transforming precision medicine for the brain through at-home EEG and cardiopulmonary data platforms for clinical development, diagnostics, and digital health. The Staff Software Engineer will lead the design and scaling of backend data infrastructure for the Datastore, including PostgreSQL models, Kafka event pipelines, and GraphQL APIs, while mentoring engineers and collaborating with product, scientific, clinical, and engineering stakeholders.

Responsibilities:

• You lead design and architecture for complex projects and systems within the Datastore, weighing implementation trade-offs with engineering principles and backing your decisions with data rather than preference
• You partner with product managers, scientific and clinical stakeholders, and other development and support teams to translate their requirements into new integrations, services, and solutions for scaling Beacon's data platform - contributing to broader product and technical decisions, not simply implementing them
• You debug and profile ambiguous problems that cross system boundaries, including codebases beyond the Datastore's purview, and you leave what you touch better than you found it — structure, test coverage, tooling
• Peers look to your work as an example: you implement features with attention to detail, demonstrating product awareness and high standards for data integrity, design soundness, and maintainability across the lifespan of the system
• You draft RFCs to lead complex feature discovery and technical design phases, soliciting and incorporating input, and helping your team align on a set of decisions to guide development
• You plan your own work and contribute to planning the team's, making reasoned trade-offs between speed, generalizability, and technical debt, and accounting for the needs of multiple stakeholder teams
• You teach and mentor other engineers regularly, give feedback that people act on, and are sought out for it. You may act as tech lead on a project or as the coordination point for engineering practice within the team
• You leverage feedback from internal stakeholders and external partners to improve operational robustness and ease of use of the Datastore and its services via new documentation, tools, and dashboards
• You can lead design across the breadth of the platform
• You can be the person the rest of the team comes to about the database
• Designing event-driven pipelines end to end: producers, consumers, replay, idempotency, and what happens when a consumer falls behind
• Leading the design of complex systems rather than individual components, across the API, the pipelines, and the services that depend on them
• Deep PostgreSQL: you reason fluently about the query planner, index selection, and EXPLAIN (ANALYZE), and why a query that should be fast isn't
• Data modeling: you design the schema itself: entities, relationships, and the constraints that make invalid states impossible. Including how to model time, since clinical results get re-scored and corrected and we need to answer "what did this look like when the report was signed?"
• Schema evolution under load: zero-downtime migrations, dual-write transitions, lock avoidance, backfill and verification. Our schema has hundreds of migrations behind it and will have hundreds more
• Operating PostgreSQL in production, not only designing for it: upgrades, replication, backup and restore, connection pooling, bloat and vacuum behaviour, extensions
• Making others good at it: you teach, review, and document, because a schema only stays coherent if the team understands it

Requirements:

• You should have experience writing complex SQL queries by hand, and should be prepared to reason about schema design, indexes, and query behavior as a matter of course
• 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 the APIs that serve them
• Highly proficient in at least one technical area relevant to this work: data modeling, distributed systems, API design, or database engineering, with the depth that others on a team come to you for
• Strong proficiency in SQL, with production experience working with PostgreSQL as an application database; you write and tune queries fluently and are comfortable reasoning about schema design, indexes, and query plans
• Hands-on experience with data streaming and event processing, using Kafka (our event bus) or an equivalent such as RabbitMQ, Pulsar, or a cloud-based equivalent like Amazon Kinesis
• Proficiency in Julia or Python
• Experience deploying and operating services in containerized environments, with familiarity in Kubernetes and Infrastructure as Code (IaC) tools such as Terraform and/or Helm
• A track record of mentoring engineers and improving how a team works - practices, tooling, test coverage, or documentation that outlasted your involvement
• A collaborative mindset: you enjoy working across disciplines and believe people achieve more together than alone
• Excellent written and verbal communication skills, especially in asynchronous and remote-friendly environments
• A self-directed approach, with a track record of thriving in hybrid or fully remote teams
• Experience with, or interest in, using LLM-assisted or agentic coding tools in a production setting, with good judgment about what guardrails are needed
• You can lead design across the breadth of the platform
• GraphQL and TypeScript/JavaScript (Node.js) - GraphQL is the Datastore's primary interface, and this profile owns significant parts of it
• Designing event-driven pipelines end to end: producers, consumers, replay, idempotency, and what happens when a consumer falls behind
• Leading the design of complex systems rather than individual components, across the API, the pipelines, and the services that depend on them
• You can be the person the rest of the team comes to about the database
• Deep PostgreSQL: you reason fluently about the query planner, index selection, and EXPLAIN (ANALYZE), and why a query that should be fast isn't
• Data modeling: you design the schema itself: entities, relationships, and the constraints that make invalid states impossible. Including how to model time, since clinical results get re-scored and corrected and we need to answer "what did this look like when the report was signed?"
• Schema evolution under load: zero-downtime migrations, dual-write transitions, lock avoidance, backfill and verification. Our schema has hundreds of migrations behind it and will have hundreds more
• Operating PostgreSQL in production, not only designing for it: upgrades, replication, backup and restore, connection pooling, bloat and vacuum behaviour, extensions
• Making others good at it: you teach, review, and document, because a schema only stays coherent if the team understands it
• Julia is a core Datastore language, and experience with Julia is a strong plus