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SeniorГибридПолная занятость

Senior Software Engineer — AdTech

Оценка ИИ

Отличная вакансия в публичной компании (NYSE: ZETA) с интересными инженерными вызовами мирового масштаба и работой в технологическом хабе Европы.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокая сложность обусловлена экстремальными требованиями к производительности (10^5–10^6 QPS) и необходимостью глубоких знаний в области низкоуровневой оптимизации Java/Go и архитектуры RTB-систем.

Анализ зарплаты

Медиана90 000 €
Рынок80 000 € – 110 000 €
Оценка ИИ

Указанная роль Senior уровня в Берлине обычно предполагает конкурентную оплату. Рыночные оценки для AdTech сектора в Германии выше среднего из-за высокой технической сложности.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Software Engineer position at Zeta Global. With over 5 years of experience in backend development and a deep focus on high-throughput, low-latency distributed systems, I am excited about the opportunity to contribute to the evolution of Zeta’s real-time bidding and decisioning systems. My background in optimizing p99 latency and managing complex microservices aligns perfectly with your requirements for building production-grade DSP components.

In my previous roles, I have successfully designed services handling high QPS and integrated real-time ML scoring models with robust safety guardrails. I am particularly impressed by Zeta's scale and its mission to simplify sophisticated marketing through AI. I am proficient in Java and Go, and I have extensive experience operating large-scale systems on AWS. I am confident that my technical expertise in performance engineering and my collaborative approach will make me a valuable asset to your engineering team in Berlin.

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Присоединяйтесь к Zeta Global и создавайте высоконагруженные системы будущего в самом сердце Берлина!

Описание вакансии

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Role

We’re looking for a Senior / Staff DSP Engineer to build and evolve Zeta’s real-time bidding and decisioning systems. This is a hands-on role focused on designing and operating low-latency, high-throughput services that process bid requests, score opportunities, apply pacing and budget constraints, and produce bids within strict SLAs.

You’ll work closely with platform, data, and ML teams to integrate retrieval and scoring systems into the bidder while keeping the hot path fast, safe, and observable. This role is ideal for engineers who love distributed systems, performance engineering, and building production-grade systems at extreme scale.

What You’ll Do

  • Real-Time Bidding Systems: Design and build core DSP bidder components that handle high-QPS bid request ingestion, filtering, enrichment, decisioning, and bid response generation within strict time budgets.
  • Auction & Eligibility Logic: Implement auction logic, campaign/line-item eligibility, targeting constraints, frequency controls, supply quality checks, and policy enforcement.
  • Low-Latency Engineering: Optimize p99 latency and tail behavior using timeouts, backpressure, concurrency control, efficient serialization, caching strategies, and careful memory/GC management.
  • Pacing & Budgeting: Build and extend pacing and budget management logic (spend controls, throttling, goal-based pacing, safety constraints) and integrate with control-plane services (campaign config, feature flags, budgets).
  • Scoring & ML Integration: Integrate real-time model scoring and feature retrieval systems with production guardrails (shadow mode, A/B tests, canaries, circuit breakers, fallbacks).
  • Reliability & Operations: Define SLOs and observability (metrics, tracing, structured logging), participate in on-call, and drive post-incident improvements.
  • AWS at Scale: Build and operate services on AWS (compute, networking, autoscaling, streaming, data stores) and partner on cost/performance optimization.

Engineering Leadership (Staff scope): For staff-level candidates, drive technical direction for parts of the bidder stack (interfaces, service boundaries, performance budgets) and mentor engineers through design/code reviews and best practices.

Senior: 5+ years of software engineering (with substantial backend/distributed systems work). Staff: 8+ years with demonstrated ownership of large-scale, production systems.

  • Proven experience building low-latency, high-QPS services (real-time decisioning, trading, ad serving, marketplaces, or similar).
  • Strong proficiency in Java and/or Go for performance-oriented backend development.
  • Solid understanding of distributed systems fundamentals: consistency trade-offs, caching, failure modes, retries/timeouts, backpressure, load shedding.
  • Experience operating production services on AWS and making practical cost/performance trade-offs.
  • Experience with data stores and patterns used in real-time systems (e.g., Redis/Memcached, DynamoDB/Cassandra, Kafka/Kinesis).
  • Strong debugging skills across services: profiling, tracing, heap/GC analysis, latency investigations.
  • Clear communicator and collaborator; able to work effectively with engineering, product, and ML partners.
  • Able to explain complex systems to mixed audiences.
  • Hands-on DSP / RTB experience: OpenRTB, exchanges, SSP integrations, seats/accounts, bid request/response handling.
  • Experience with pacing/budgeting systems, bid shading, dynamic floors, and supply quality/brand-safety gating.
  • Experience integrating ML ranking/scoring in real time (feature retrieval, embeddings, model serving, inference optimization).
  • Experience building privacy-aware systems (consent/identity signals, PII-safe logging, governance controls).
  • Experience operating extremely high-scale systems (10^5–10^6+ QPS), multi-region design, or hybrid/colocation architectures.
  • Kubernetes/service mesh familiarity and/or deep Linux networking/performance knowledge (eBPF is a plus).

BENEFITS & PERKS

  • Excellent medical, dental, and vision coverage

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases

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Навыки

  • AWS
  • Kubernetes
  • Microservices
  • Redis
  • Kafka
  • Distributed Systems
  • Java
  • DynamoDB
  • Go
  • AdTech
  • Cassandra
  • Low Latency
  • RTB

Возможные вопросы на собеседовании

Вакансия требует работы с высоконагруженными системами, где важна каждая миллисекунда.

Как бы вы подошли к оптимизации p99 задержки в Java-приложении, работающем под нагрузкой в 100к QPS?

RTB-системы требуют строгой синхронизации бюджетов.

Опишите стратегии управления бюджетом и пейсингом в распределенной системе, чтобы избежать перерасхода (overspend).

Работа предполагает использование AWS.

Какие архитектурные паттерны в AWS вы бы использовали для обеспечения отказоустойчивости системы в нескольких регионах при сохранении минимального latency?

Упоминается интеграция ML-моделей в горячий путь обработки запроса.

Как интегрировать ML-скоринг в реальном времени, чтобы он не блокировал основной поток обработки ставок?

Проверка знаний фундаментальных основ распределенных систем.

Расскажите о компромиссах между консистентностью и доступностью (CAP-теорема) при выборе хранилища для данных о частоте показов (frequency capping).

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