Senior Software Engineer – Forecasting & Recommendations
About this role
Zeta Global develops backend services and distributed systems that power Forecasting, Recommendations, and Performance Advisor platforms. The engineering team focuses on designing high-throughput, low-latency SaaS infrastructure, APIs, and event-driven pipelines that integrate production-grade AI and machine-learning capabilities at scale.
As a Senior Software Engineer, the selected candidate will write production-quality code daily, own significant backend components from design to deployment, and collaborate closely with data science, platform, and product teams. The role requires designing resilient cloud-native architectures, championing system observability, and resolving production incidents to support data-driven decisioning systems.
Ideal candidates bring substantial professional software engineering experience building scalable backend services and distributed architectures. Strong judgement in reliability, performance optimization, and pragmatic problem-solving will enable engineers to succeed in this environment.
What you will do
- Design, build, and operate backend services supporting forecasting, recommendations, and Performance Advisor workflows in production.
- Write production-quality code daily and own significant services and components from design through deployment and incident resolution.
- Build low-latency, high-throughput SaaS services, including APIs, event-driven pipelines, data-processing services, and model-integration workflows.
- Collaborate with data science, platform, and product teams to integrate forecasting, recommendation, and AI decisioning capabilities into production systems.
- Ensure product quality through rigorous code reviews, unit tests, debugging, and continuous improvement.
- Champion observability and reliability through metrics, logging, tracing, alerting, and root-cause analysis.
- Participate in production incident response, retrospectives, and systemic fixes while reducing complexity across services and infrastructure.
What you need
- Possess 8 to 12 years of professional software engineering experience building and operating scalable, robust backend systems.
- Demonstrate a strong track record of hands-on ownership of production services with improvements in performance, reliability, scalability, or cost.
- Exhibit strong expertise in distributed systems design, including service boundaries, concurrency, data modeling, failure handling, and scalability tradeoffs.
- Show experience developing high-throughput, event-driven systems and backend APIs alongside container-based solutions and microservices architectures.
- Bring strong hands-on experience with AWS, cloud-native system design, Kafka, Elasticsearch, NoSQL databases such as Aerospike, Thrift, and modern CI/CD pipelines.
- Hold proficiency in one or more backend languages commonly used for large-scale systems, such as Python, Java, Ruby, Go, or Scala.
- Possess a bachelor’s or master’s degree in computer science, mathematics, engineering, or a related field, or equivalent practical experience.
Nice to have
- Experience supporting machine-learning-driven systems, forecasting, recommendations, or similar data-driven applications
- Familiarity with modern AI-enabled applications, LLM integrations, or agentic workflows
Skills and tech stack
Questions candidates ask
What kind of systems will I be building in this role?
You will design, build, and operate backend services supporting forecasting, recommendations, and Performance Advisor workflows in production. This includes building low-latency, high-throughput SaaS services, APIs, event-driven pipelines, and model-integration workflows.
What technologies and tools are used by the team?
The tech stack includes Kafka, Elasticsearch, NoSQL databases such as Aerospike, Thrift, and AWS for cloud-native system design. Engineers also work with backend languages commonly used for large-scale systems like Python, Java, Ruby, Go, or Scala.
What level of experience is required for this position?
The role requires 8 to 12 years of professional software engineering experience building and operating scalable, robust backend systems. Candidates must also have a strong track record of hands-on ownership of production services.
Who will I be collaborating with on a regular basis?
You will collaborate effectively across engineering and product teams in a large, distributed development environment. Specifically, you will work with data science, platform, and product teams to integrate AI decisioning capabilities into production systems.