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Paramount Global

Joint venture producing film, television content

Sr. Software Engineer Cloud infrastructure

Backend EngineeringUpdated on Nov 14
Full-timeBurbank, CA, USA

About the Job

Requirements

  • The role requires hands-on experience in both software engineering and cloud infrastructure.
  • At least 7 years of experience in software engineering and cloud infrastructure, with at least 3 years in each area.
  • Demonstrated expertise in optimizing data streaming applications on cloud infrastructure.
  • A proven track record building and operating production-grade real-time data platforms.
  • Experience mentoring engineers and collaborating across teams.
  • A bachelor's degree in Computer Science, Engineering, or a related field is required.
  • Deep expertise in optimizing data streaming applications for throughput, latency, and cost efficiency across cloud environments.
  • Proficiency in tuning Kafka producers, consumers, and brokers, including batch sizing, compression, partition strategies, and consumer lag management, within cloud-hosted deployments.
  • Experience leveraging cloud-native managed services such as Kafka, GCP Pub/Sub, BigQuery Streaming, and OCI Streaming to complement or extend Kafka-based pipelines.
  • Skill in right-sizing and dynamically allocating cloud compute resources, including virtual machines, node pools, and spot or preemptible instances, to match streaming workload profiles and reduce infrastructure spend.
  • Familiarity with cloud-native autoscaling patterns for streaming consumers, including KEDA, HPA, and custom metrics-based scaling in Kubernetes.
  • Ability to identify and address performance bottlenecks at the intersection of application code and cloud resource limits.
  • Experience benchmarking and profiling streaming pipelines end to end, and translating findings into infrastructure and code improvements.

Responsibilities

  • Own end-to-end performance of data streaming applications running on cloud infrastructure, from Kafka topic configuration through consumer processing and downstream delivery.
  • Profile and tune streaming pipelines to maximize throughput and minimize latency using cloud-native compute, storage, and networking resources.
  • Identify and address bottlenecks between application code and cloud resource constraints, including CPU throttling, network saturation, I/O limits, and memory constraints.
  • Design and implement cloud resource utilization strategies, including spot or preemptible instances, managed streaming services, and dynamic node pool scaling to balance performance with cost efficiency.
  • Benchmark streaming pipelines end to end and translate findings into actionable infrastructure and code improvements.
  • Collaborate with Data and AI/ML engineering teams to ensure streaming pipelines are optimally provisioned for real-time feature engineering, inference, and analytics workloads.
  • 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.
  • Develop reusable streaming frameworks, libraries, and platform capabilities to improve engineering velocity and standardization.
  • Architect, implement, and optimize multi-cloud infrastructure across GCP and OCI for large-scale data and streaming workloads.
  • Design and implement advanced networking architectures, including VPC peering, VPNs, load balancers, and cross-region failover strategies.
  • Build and maintain Terraform-based infrastructure-as-code frameworks to standardize deployments and enable developer self-service.
  • Define autoscaling, deployment, failover, and resource optimization strategies for high-volume production systems.
  • Contribute to platform-wide architecture decisions related to scalability, resiliency, high availability, and disaster recovery.
  • Deploy and manage containerized microservices in Kubernetes environments (GKE and OKE) across cloud platforms.
  • Implement container orchestration best practices, service-mesh configurations, and rolling-deployment strategies.
  • Partner with platform engineering teams to improve developer tooling, deployment automation, and runtime reliability.
  • Ensure production-grade reliability, observability, and operational maturity across streaming platforms and infrastructure.
  • Implement comprehensive observability using Prometheus, Grafana, centralized logging, distributed tracing, and health monitoring.
  • Optimize systems for throughput, latency, resiliency, resource efficiency, and cloud cost governance.
  • Build automated testing strategies for streaming and infrastructure workflows, including unit, integration, contract, chaos, and performance testing.
  • Lead incident response, root-cause analysis, and postmortems to improve uptime and reduce operational risk.
  • Partner with Data Engineering teams to integrate streaming architectures with batch processing, 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.
  • Communicate technical tradeoffs, scalability considerations, and operational risks to engineering stakeholders.
  • Lead architectural discussions, design reviews, and technical deep dives across distributed systems and cloud infrastructure.
  • Drive engineering standards for code quality, documentation, observability, security, and platform maintainability.
  • Influence long-term technical strategy and modernization initiatives for real-time data infrastructure and cloud platforms.

Desired Qualifications

  • An advanced degree is preferred.

About the Company

Paramount Global

Joint venture producing film, television content

Preparing a concise company summary based on the provided description.

Company size
10,001+
Company stage
IPO
Headquarters
Los Angeles, CA
Founded
1912

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