Senior Developer Operations Engineer

Unity Unity · Enterprise · Tel Aviv, Israel · Engineering

Senior DevOps Engineer to join Aura, a large-scale mobile content discovery platform. The role involves designing and implementing scalable data and AI/ML infrastructure, improving data pipeline deployment and monitoring, building robust data/ML platforms, and collaborating with ML and Backend engineers. Focus on Kubernetes, IaC, cloud platforms, and observability for AI/ML workloads.

What you'd actually do

  1. Design and implement scalable data and AI/ML infrastructure across multiple environments using Kubernetes, orchestration platforms, and IaC to power our AI, ML, and analytics ecosystem
  2. Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities that empower data teams to deliver insights faster with higher reliability
  3. Build and optimize infrastructure that supports diverse data workloads from real-time streaming to batch processing, ensuring performance and cost-effectiveness for critical analytics systems
  4. Collaborate with engineering leaders across backend and ML teams, champion modern infrastructure practices, and mentor team members to elevate how we build, deploy, and operate data systems at scale
  5. Collaborate on high-level technical designs with ML and Backend engineers to build resilient systems

Skills

Required

  • 5+ years of hands-on DevOps experience building, shipping, and operating production systems
  • Infrastructure as Code: design and implement infrastructure automation using tools such as Terraform, Pulumi, or CloudFormation (modular code, reusable patterns, pipeline integration)
  • Cloud platforms: deep experience with AWS, GCP, or Azure (core services, networking, IAM)
  • Kubernetes: strong end-to-end understanding of Kubernetes as a system (routing/networking, scaling, security, observability, upgrades), with proven experience integrating data-centric components (e.g., Kafka, RDS, BigQuery, Aerospike).
  • GitOps & CI/CD: practical experience implementing pipelines and advanced delivery using tools such as Argo CD / Argo Rollouts, GitHub Actions, or similar
  • Observability: metrics, logs, and traces; actionable alerting and SLOs using tools such as Prometheus, Grafana, ELK/EFK, OpenTelemetry, or similar
  • Scalability & Performance: Proven experience managing production environments characterized by high traffic volumes and large amounts of data, with a focus on maintaining system reliability and cost-efficiency at scale.

Nice to have

  • Coding proficiency in at least one language (e.g., Python or TypeScript); able to build production-grade automation and tools.
  • Data Pipeline Orchestration: Demonstrated success building and optimizing data pipeline deployment using modern tools (Airflow, Temporal, Kubernetes operators) and implementing GitOps practices for data workloads
  • Data Engineer Experience Focus: Track record of creating and improving self-service platforms, deployment tools, and monitoring solutions that measurably enhance data engineering team productivity
  • Data Infrastructure Deep Knowledge: Extensive experience designing infrastructure for data-intensive workloads including streaming platforms (Kafka, Kinesis), data processing frameworks (Spark, Flink), storage solutions, and comprehensive observability systems

What the JD emphasized

  • AI/ML infrastructure
  • data pipeline deployment
  • data workloads
  • ML and Backend engineers

Other signals

  • Design and implement scalable data and AI/ML infrastructure
  • Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities
  • Build and optimize infrastructure that supports diverse data workloads
  • Collaborate on high-level technical designs with ML and Backend engineers