Staff Backend Software Engineer

Fox•Tempe, NY

1d•Hybrid

About The Position

We are looking for a Staff Backend Software Engineer to lead delivery of reliable, scalable services for audio/video hosting, monetization, and analytics in the media and creator space at a global footprint scale. You will work primarily in Python on APIs, asynchronous background job orchestration, infrastructure-as-code, and data pipelines alongside Celery, PostgreSQL, Redis, RabbitMQ, and cloud primitives on AWS and GCP, partnering with product, frontend, and infrastructure engineers.

Requirements

10+ years of professional backend or backend-heavy engineering experience in platform systems, or equivalent depth, including meaningful ownership of large services or platforms.
Strong Python and a track record designing service-oriented systems: APIs, background jobs, idempotency, failure modes, and capacity thinking.
Deep hands-on experience with Celery (or similar) and message brokers (AMQP and RabbitMQ-class), including heartbeats, retries, queue splits, and operational debugging.
Strong SQL (PostgreSQL) and practical Redis usage beyond simple caching: coordination, rate limiting, circuit breaking, and correctness under concurrency.
Substantial cloud infrastructure experience with GCP and AWS (for example AWS: EC2, ALB, IAM, CloudWatch, S3, RDS and proxy patterns) and comfort leading Terraform changes.
Substantial experience with CI/CD in cloud environment.
Demonstrated leadership in security-minded engineering: scoped IAM, TLS for data stores, network egress discipline, WAF and rate-limiting concepts.
Excellent judgment under ambiguity, written communication, and a record of raising team quality without blocking delivery.
Experience successfully driving cross-team alignment to get impactful work shipped, and work with influence over authority.
Demonstrated experience establishing engineering standards, component architectures, and development best practices

Nice To Haves

Container building, deployment (Docker, ECS, Kubernetes), and multi-cloud networking a strong plus.
Experience with authorization at scale (RBAC, ABAC, or policy engines) is a strong plus.
Experience in early start-up or otherwise fast moving, rapidly evolving environments, and have ideally built products from 0 to 1.
Druid, BigQuery, or large analytical query constraints
Elastic Beanstalk exposure
Media, entertainment, creator economy, CTV, OTT, SSAI, VAST, CDN, edge processing

Responsibilities

Lead design and implementation of RESTful APIs and async workers(Celery) for publishing, media processing, SSAI and VAST-related workflows, imports, and reporting.
Own reliability and performance of distributed systems and workloads: queue design, deduplication, chord and dispatch patterns, prefetch tuning, backpressure, and operational logging that supports incident response.
Drive data and integration decisions across PostgreSQL, Redis-backed coordination, object storage, and systems such as data warehouse-backed reporting (batching, limits, and cost-aware query patterns).
Lead or significantly contribute to Terraform and AWS changes affecting the application: EB tiers, IAM, networking, WAF and rate limits, observability hooks, and phased rollouts.
Raise the bar on observability: structured logging, Prometheus-style metrics, Grafana and CloudWatch usage, and alarms wired to actionable on-call signals
Define and evolve testing strategy for critical paths (unit and regression around task helpers and services, AI-driven testing), CI-friendly checks, and operational readiness for risky migrations.
Represent backend concerns in planning, design reviews, and incident-style discussions; communicate trade-offs clearly to technical and non-technical partners.
Anticipate scaling and complexity: propose phased migrations, safer defaults, and long-term maintainability over short-term hacks.
Help define how AI is integrated into backend development to unlock smarter execution, faster delivery, and automation at scale, while ensuring responsible and secure use of these technologies.
Establish best practices for AI-assisted coding tools to accelerate development while maintaining high standards for correctness, security, and performance.
Explore and guide how AI can assist in system design, performance tuning, and anomaly detection across distributed backend systems.
Drive improvements in internal tools and workflows that leverage AI to enhance productivity, code quality, and observability, helping evolve backend engineering culture into an AI-native practice.