Sr. Software Engineer, Data Streaming Systems

We are Paramount on a mission to unleash the power of content. We’ve got the brands, we’ve got the stars, and we have the power to achieve our mission to entertain the planet – now we’re missing you. Becoming a part of Paramount means joining a team of passionate people who recognize the power of content and enjoy a touch of fun and uniqueness.

Together, we co‑create moments that matter for our audiences and our employees, and aim to leave a positive mark on culture.

This role builds fault‑tolerant streaming applications supporting real‑time analytics, APIs, AI workflows, and critical data services. You will design and build high‑performance, fault‑tolerant streaming applications that power real‑time analytics, APIs, AI workflows, and mission‑critical data services across the organization. As a senior engineer, you will set the technical direction, mentor other engineers, and ensure our streaming platforms are reliable, scalable, and operate smoothly.

• Design & Build Real‑Time Streaming Applications
  • Develop high‑throughput, low‑latency streaming applications using Java and Kafka.
  • Design event‑driven microservices that process, enrich, and route real‑time data at scale.
  • Implement reactive, non‑blocking architectures to support high concurrency and resilience.
  • Create reusable streaming frameworks and libraries to improve engineering efficiency and standardization across the platform.
• Architect Scalable Distributed Systems
  • Design and optimize distributed streaming architectures, including Kafka topics, partitioning strategies, consumer groups, schema management, and event lifecycle patterns.
  • Help make architecture decisions for the whole platform, covering scalability, resiliency, high availability, disaster recovery, and multi‑region deployments.
  • Drive best practices around event modeling, schema evolution, idempotency, replayability, and data consistency across streaming systems.
  • Build and optimize horizontally scalable services deployed within Kubernetes‑based cloud environments.
  • Ensure streaming platforms are reliable and easy to monitor; keep systems operationally mature.
  • Optimize systems for throughput, latency, resiliency, resource efficiency, and infrastructure cost management; set up full observability using metrics and centralized logging, distributed tracing, alerting, and health monitoring tools.
  • Build automated testing strategies for streaming workflows, including unit, integration, contract, chaos, and performance testing.
  • Participate in production support, incident response, root‑cause analysis, and continuous reliability improvement initiatives.
• Cloud‑Native & Kubernetes Engineering
  • Deploy and manage containerized microservices in Kubernetes across GCP, AWS, or similar cloud platforms.
  • Define strategies for autoscaling, deployment, failover, and resource optimization for high‑volume production systems.
  • Create and manage CI/CD pipelines, including Infrastructure‑as‑Code and automated deployment workflows.
  • Collaborate with platform engineering teams to improve developer tooling, deployment automation, and runtime reliability.
• Cross‑Functional Collaboration
  • Partner with Data Engineering teams to integrate streaming architectures with batch processing systems, data lakes, and analytical platforms.
  • Collaborate with Software Engineering, Product Management, and API teams to enable real‑time services and data‑driven applications.
  • Work with AI/ML engineering teams to support real‑time feature engineering, inference pipelines, and operational AI workloads.
  • Clearly explain technical tradeoffs, scalability considerations, and operational risks to engineering stakeholders.

• Java & Reactive Programming
  • Advanced proficiency in Java, including concurrency, multithreading, and JVM performance tuning.
  • Strong experience with reactive frameworks such as Spring Web Flux, Project Reactor, or similar.
  • Deep knowledge of asynchronous, non‑blocking system design.
  • Extensive experience with Apache Kafka (producers, consumers, streams, schema registry).
  • Strong knowledge of partitioning strategies, offset management, rebalancing, and failure recovery.
  • Experience designing event…