Staff Software Engineer

🕒 il y a 10 jours

🗽 New York – Distant

🗽 New York – Remote

💵 $160 000 - $180 000 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

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Zeta Global

Site WebLinkedInTous les Emplois

1001 - 5000 employés

Fondée en 2007

☁️ SaaS

🤖 Intelligence artificielle

🤝 B2B

💰 Post-IPO Debt en 2024-09

SaaS • Artificial Intelligence • B2B

Zeta Global est une plateforme marketing alimentée par l'IA qui exploite une IA propriétaire et des billions de signaux de consommateurs pour acquérir, développer et fidéliser les clients de manière plus efficace. La Zeta Marketing Platform (ZMP) propose une suite complète d'outils, notamment la gestion de données, des plateformes de données clients (CDP), des fournisseurs de services de messagerie (ESP) et le traitement de signal numérique (DSP), afin de créer des expériences client individualisées et d'améliorer les résultats marketing. Zeta met l'accent sur le marketing omnicanal, l'intelligence client et les stratégies marketing basées sur les données, en partenariat avec des marques, des agences et des éditeurs du monde entier pour accélérer la croissance et l'engagement des marques. Leur plateforme est conçue pour résoudre des défis marketing complexes avec des solutions pour l'acquisition, le développement et la fidélisation de la clientèle grâce à l'IA prédictive et aux données consommateurs exploitables.

Description

• Identify systemic engineering challenges across our platforms and drive their resolution — shaping the technical backlog and near-term architecture. • Propose and validate technical approaches for problems involving scale, performance, security, or cross-team dependencies. • Lead architectural decisions for complex, ambiguous, or high-risk initiatives. • Incorporate modern industry patterns — including AI/ML tooling — into our technical strategy where it genuinely moves the needle. • Write code, review PRs, debug production issues, and optimize system performance — this is not a whiteboard-only role. • Dive deep into our AWS infrastructure, Kubernetes workloads, and JVM-based services to find and fix what's actually wrong. • Participate in our on-call rotation as a second-level escalation point for complex engineering incidents. • Step in during large incidents to help teams triage, coordinate, and resolve — and follow through with post-incident reviews that drive lasting fixes. • Champion operational excellence across our engineering teams: observability, reliability, deployment practices, and the operational habits that keep systems healthy at scale. • Partner with engineering teams as a technical point of contact on complex projects — ensuring good architectural decisions get documented and don't have to be re-litigated. • Work directly with Engineering Managers to align technical work with team and product priorities. • Mentor engineers across our teams, raising the technical floor through reviews, pairing, and direct feedback. • Stay close to the customer. Understand how Sailthru's platform affects the people using it, bring that context into technical decisions, and push back when engineering choices create friction for customers. • Partner closely with the product team — contribute to shaping what gets built, not just how. The best technical decisions happen when engineering and product are thinking together from the start.

🎯 Exigences

• Deep, battle-tested expertise in AWS and cloud-native architecture — you know how to build reliable, scalable systems at real traffic volumes. • Strong JVM fundamentals, primarily Java and Kotlin, with the range to work across languages as the problem demands. • Hands-on Kubernetes experience — deploying, operating, and troubleshooting workloads in production. • Experience designing and operating distributed systems — fault tolerance, consistency tradeoffs, service coordination, and failure modes at scale. • Comfort working with large databases at scale — query optimization, schema design, operational concerns, and the tradeoffs between data storage approaches. • Practical experience with AI/ML engineering — whether integrating LLM capabilities, building pipelines, or evaluating where AI actually helps vs. adds complexity. • A track record of delivering large, complex initiatives in ambiguous environments — scoping, driving alignment, and shipping. • Strong debugging instincts and a bias toward root-cause analysis over symptomatic fixes. • The judgment to know when to push for the right solution and when pragmatism wins. • Fluency with AI-driven development tools — Claude Code, GitHub Copilot, Codex, or similar — is a firm requirement. We use these tools daily and expect staff engineers to be effective with them and to model best practices around prompt discipline, output validation, and security. • Comfort operating without a playbook — you've solved novel problems before and know how to navigate uncertainty. • Comfort working in and around legacy codebases — you can read unfamiliar code, understand systems that predate you, and make meaningful improvements without needing a rewrite. • The ability to quickly map an existing architecture — identify bottlenecks, constraints, and opportunities — and turn that understanding into concrete, actionable proposals that increase team velocity. • You think big. You're not constrained by how things are done today — you can envision meaningfully better outcomes and work backwards from them to find a path. • Bias for action. You'd rather move with imperfect information and course-correct than wait for certainty that never comes. You know the difference between a reversible decision and one that warrants more care.

🏖️ Avantages

• Unlimited PTO • Excellent medical, dental, and vision coverage • Employee Equity and Stock Purchase Plan • Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!