Staff Software Engineer

Zeta Global is an AI-Powered Marketing Cloud company that utilizes advanced artificial intelligence to enhance marketing efficiency. The Staff Software Engineer role involves tackling complex engineering challenges, leading architectural decisions, and contributing hands-on to high-priority initiatives across multiple platforms.

Responsibilities:

• 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

Requirements:

• 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
• Experience with PHP or Python is a plus
• Familiarity with high-volume messaging infrastructure or email/marketing tech is welcome but not required
• Knowledge of the Databricks platform and data engineering practices (pipelines, lakehouses, batch and streaming processing) is a bonus