Senior Backend Engineer job at Meltwater Media in Redwood City, CA

Title: Senior Backend Engineer

Location: Redwood City, CA, United States

Category: Engineering

Description

About the Role

We're looking for a Senior Backend Engineer to build and scale the GenAI Lens backend platform. This is a production-focused role centered on MongoDB data design and performance, scalable/durable data ingestion and processing pipelines, and operating high-throughput systems with strong observability and reliability.

You'll also help build and evolve our API layer (GraphQL and JSON-RPC). Experience with GraphQL is a strong plus, but the core focus is data systems and platform engineering. You should be comfortable working in a product that leverages modern AI technologies (embeddings, vector search, LLM integrations) and understand the practical standards that come with them-evaluation, guardrails, observability, and cost controls.

What You'll Work On

Maintain and improve our data pipelines to keep data flowing reliably from ingestion to delivery

Scalable, durable data ingestion and processing pipelines (event-driven, fault-tolerant workflows; retries, idempotency, backfills, and DLQs)

Own data quality by implementing monitoring, alerting, and validation

Design MongoDB schemas and query/index strategies for scale (aggregation pipelines, Atlas Search/vector search where relevant)

JSON-RPC data layer service that powers GraphQL (designing/maintaining RPC methods, scaling throughput/latency, and evolving contracts safely)

Support AI-adjacent platform needs (embedding generation workflows, provider abstractions, prompt/model metadata, evaluation hooks)

Responsibilities

Own backend services end-to-end, from design and implementation through deployment and production support

Deliver scalable GraphQL resolvers with performance-aware patterns (batching, caching, pagination)

Build and evolve the JSON-RPC data access layer for GraphQL, including method design, backward compatibility, and performance tuning.

Own MongoDB performance: modeling, indexing, aggregation pipelines, and measurable latency/throughput targets

Build reliable serverless/event-driven components (idempotency, retries, DLQs, backpressure, rate limiting)

Improve reliability and operational readiness (SLO-minded engineering, incident response hygiene, runbooks)

Partner closely with frontend, product, and UX to enable features cleanly and safely

Drive code quality via testing, reviews, and CI/CD improvements

Mentor engineers and influence engineering standards across the team

Required Skills & Experience

Experience building and maintaining backend services in production

Strong experience with GraphQL APIs (schema design, resolver patterns, authorization, performance)

Strong experience with MongoDB (data modeling, indexing, aggregation pipelines; Atlas Search and Vector Search, sharding experience is a plus)

Strong proficiency with Node.js and JavaScript / TypeScript

Strong proficiency with Python (services, jobs, data processing, or tooling)

Experience building on AWS, preferably serverless/event-driven architectures (Lambda, SQS/SNS/EventBridge, S3)

Experience working with high-traffic or business-critical systems

Solid understanding of performance optimization techniques (caching, async processing, data access patterns)

Experience with Terraform for production infrastructure (IAM, networking, secrets, repeatable environments)

Experience with testing frameworks and pragmatic test strategy (unit, integration, contract)

Familiarity with CI/CD pipelines (GitHub Actions preferred; alternatives acceptable)

Experience debugging, monitoring, and operating production systems

Comfortable working in a distributed team environment

Strong operational skills: logging, metrics, tracing, dashboards/alerts, and production support practices

Infrastructure & Platform Experience

Cloud experience, preferably AWS, including:

Lambda

SQS / SNS / EventBridge / Kafka

S3

CloudWatch

API Gateway (where applicable)

Experience with Infrastructure as Code, preferably Terraform

Experience with private networking patterns (VPC, security groups, PrivateLink/VPC endpoints) is a plus

Exposure to containerized workloads (EKS/Kubernetes) is a plus, even if the core architecture is serverless-first

Collaboration & Others

Prior experience leading projects, features, or technical initiatives

Ability to influence architecture and design decisions

Strong ownership mindset and ability to identify and execute improvements independently

AI & Developer Productivity

Familiarity with embeddings and vector search concepts, and the tradeoffs they introduce (latency, cost, relevance)

Experience integrating with LLM/embedding providers via clean abstractions (timeouts, retries, fallbacks, rate limits)

Awareness of AI industry standards/practices: prompt/version management, evaluation/regression checks, basic guardrails, and auditability

Hands-on experience using AI tools to improve development workflows and code quality (e.g., C