Senior Backend Engineer

Description

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  Maintain and improve our data pipelines to keep data flowing reliably from ingestion to delivery
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  Scalable, durable data ingestion and processing pipelines (event-driven, fault-tolerant workflows; retries, idempotency, backfills, and DLQs)
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  Own data quality by implementing monitoring, alerting, and validation
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  Design MongoDB schemas and query/index strategies for scale (aggregation pipelines, Atlas Search/vector search where relevant)
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  JSON-RPC data layer service that powers GraphQL (designing/maintaining RPC methods, scaling throughput/latency, and evolving contracts safely)
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  Support AI-adjacent platform needs (embedding generation workflows, provider abstractions, prompt/model metadata, evaluation hooks)

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  Own backend services end-to-end, from design and implementation through deployment and production support
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  Deliver scalable GraphQL resolvers with performance-aware patterns (batching, caching, pagination)
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  Build and evolve the JSON-RPC data access layer for GraphQL, including method design, backward compatibility, and performance tuning.
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  Own MongoDB performance: modeling, indexing, aggregation pipelines, and measurable latency/throughput targets
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  Build reliable serverless/event-driven components (idempotency, retries, DLQs, backpressure, rate limiting)
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  Improve reliability and operational readiness (SLO-minded engineering, incident response hygiene, runbooks)
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  Partner closely with frontend, product, and UX to enable features cleanly and safely
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  Drive code quality via testing, reviews, and CI/CD improvements
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  Mentor engineers and influence engineering standards across the team

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  Experience building and maintaining backend services in production
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  Strong experience with GraphQL APIs (schema design, resolver patterns, authorization, performance)
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  Strong experience with MongoDB (data modeling, indexing, aggregation pipelines; Atlas Search and Vector Search, sharding experience is a plus)
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  Strong proficiency with Node.js and JavaScript / TypeScript
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  Strong proficiency with Python (services, jobs, data processing, or tooling)
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  Experience building on AWS, preferably serverless/event-driven architectures (Lambda, SQS/SNS/EventBridge, S3)
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  Experience working with high-traffic or business-critical systems
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  Solid understanding of performance optimization techniques (caching, async processing, data access patterns)
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  Experience with Terraform for production infrastructure (IAM, networking, secrets, repeatable environments)
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  Experience with testing frameworks and pragmatic test strategy (unit, integration, contract)
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  Familiarity with CI/CD pipelines (GitHub Actions preferred; alternatives acceptable)
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  Experience debugging, monitoring, and operating production systems
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  Comfortable working in a distributed team environment
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  Strong operational skills: logging, metrics, tracing, dashboards/alerts, and production support practices

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  Cloud experience, preferably AWS, including:
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  Lambda
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  SQS / SNS / EventBridge / Kafka
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  S3
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  CloudWatch
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  API Gateway (where applicable)
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  Experience with Infrastructure as Code, preferably Terraform
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  Experience with private networking patterns (VPC, security groups, PrivateLink/VPC endpoints) is a plus
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  Exposure to containerized workloads (EKS/Kubernetes) is a plus, even if the core architecture is serverless-first

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  Prior experience leading projects, features, or technical initiatives
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  Ability to influence architecture and design decisions
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  Strong ownership mindset and ability to identify and execute improvements independently

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  Familiarity with embeddings and vector search concepts, and the tradeoffs they introduce (latency, cost, relevance)
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  Experience integrating with LLM/embedding providers via clean abstractions (timeouts, retries, fallbacks, rate limits)
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  Awareness of AI industry standards/practices: prompt/version management, evaluation/regression checks, basic guardrails, and auditability
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  Hands-on experience using AI tools to improve development workflows and code quality (e.g., Copilot, Claude Code, OpenAI-based tools)
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  Ability to articulate how AI has improved development speed, quality, testing, or maintainability

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  Experience building high-throughput services (e.g., millions of requests/minute class systems) and tuning concurrency/connection pools
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  Experience designing "saved filter" or "materialized view" style performance optimizations for analytics/trends workloads
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  Experience with multi-region data and compliance-minded architectures
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  Experience refactoring legacy systems or consolidating services for efficiency and cost savings

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  Help build a modern backend platform where quality and scalability are non-negotiable, enabling a high-impact product for the company with visible outcomes and real customer adoption.
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  Work with cutting-edge technologies and modern AI tooling (embeddings, vector search, LLM providers, evaluation/guardrails) while staying grounded in strong backend engineering practices.
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  Supportive, collaborative team culture-ideas are heard, discussed openly, and improved together as a group

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  Enjoy comprehensive paid time off that allows you to have an enhanced work-life balance
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  Excellent medical, dental, and vision options
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  401(k) matching, life insurance, commuter benefits, and parental leave plans
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  Complimentary CalmApp subscription for you and your loved ones, because mental wellness matters.
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  Energetic work environment with a hybrid work style, providing the balance you need.
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  Thrive within our inclusive community and seize ongoing professional development opportunities to elevate your career.
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  Total compensation range for this position: $188,000-$250,000 USD per year.