Top Engineering Jobs

Lead technical execution and architecture for the Loyalty product. Own roadmap, design distributed services and APIs, ensure reliability/observability, integrate with ZMP and AthenaOS, build and coach engineering teams, and drive cross-team delivery of scalable multi-tenant loyalty capabilities.

Lead technical execution, architecture, roadmap, and delivery for the Loyalty product. Manage and grow engineering teams, drive design and operational excellence, ensure reliability/observability, coordinate cross-team integrations, and deliver scalable multi-tenant SaaS features integrated with Zeta’s platform and AI capabilities.

Provide technical leadership for real-time data collection and delivery systems. Design, develop, tune, and maintain low-latency distributed infrastructure and data-processing services on cloud platforms. Collaborate with product, DevOps, engineering, and executives on roadmaps and data products; participate in architecture decisions, mentor developers, scale infrastructure, and support on-call operations.

Build and operate data platform infrastructure supporting analytics, machine learning, and AI systems. Responsibilities include developing Lakehouse and Spark systems, improving performance and reliability, implementing data quality, governance, lineage, metadata, security, and access controls, evaluating query and OLAP technologies, enhancing observability and deployment processes, supporting incident response, collaborating with data teams, contributing to open-source projects, and mentoring engineers.

Support CRM quality assurance during client migrations and onboarding. Execute testing according to STLC standards, validate applications, tools, APIs, landing pages, and database imports/exports, document test scenarios, reproduce production issues, and follow change-control processes. Collaborate with Solutions Management, Account teams, and development while delivering quickly and independently or within Agile and traditional project environments.

Design, build, and operate high-throughput backend services for forecasting, recommendations, and AI-driven decisioning. Own services from architecture through deployment and incident resolution, using distributed systems, APIs, event-driven pipelines, cloud infrastructure, and machine-learning integrations. Improve scalability, reliability, observability, and performance while collaborating with data science, platform, and product teams. Mentor engineers and contribute to design reviews, production support, and systemic improvements.

Provides hands-on technical leadership for forecasting, recommendation, and agentic AI platforms. Designs, builds, and operates high-throughput, low-latency distributed systems, APIs, data pipelines, model inference services, and agent orchestration. Owns services through production operations, on-call response, observability, incident resolution, scalability improvements, and architectural reviews. Guides cross-team technical direction and delivers reliable cloud-native systems across AWS and on-premises environments.

Design and build backend services and APIs for identity resolution and graph integrations. Optimize event-driven pipelines processing billions of signals, deploy cloud-native services on AWS EKS/Kubernetes with Helm, and ensure reliability, observability, and scalability. Collaborate across engineering, product, and data teams while mentoring junior engineers and advancing distributed identity platform architecture.

Design and build backend services and APIs for identity resolution and platform integrations. Optimize event-driven pipelines processing billions of signals, deploy cloud-native services on AWS EKS and Kubernetes, and ensure reliability, observability, and scalability. Collaborate with engineering, product, and data teams to deliver identity solutions while mentoring junior engineers. The role requires expertise in backend or data engineering, distributed systems, streaming technologies, cloud deployment, data modeling, and high-throughput databases.

Designs and maintains scalable automation frameworks, tools, and scripts; architects QA automation solutions; improves testing processes and tooling; integrates automation into CI/CD and DevOps environments; resolves complex quality issues; tracks QA KPIs; mentors engineers; and evaluates AI/ML technologies for intelligent testing and automation.