Senior Data Ops Engineer, Data Activation & Products - Activision

Activision is a developer, publisher, and distributor of interactive entertainment and gaming experiences. The company is seeking a Senior Data Ops Engineer to improve the reliability, observability, and operational maturity of data platforms, Kubernetes-based systems, internal applications, and cloud environments. The role focuses on incident response, monitoring, automation, deployment reliability, and collaboration with engineering teams across data and platform services.

Responsibilities:

• Monitor service health, respond to alerts, and participate in incident response for cloud, Kubernetes, application, and data platform environments
• Investigate reliability issues across Kubernetes, networking, DNS, application runtime behavior, Databricks jobs, Spark workloads, orchestration systems, event systems, and dependent services
• Support the reliability of internal applications, APIs, workers, streaming consumers, event-driven services, and deployment workflows used by data engineering and business teams
• Build and maintain dashboards, alerting, runbooks, and operational documentation that improve detection and recovery speed
• Improve observability for Databricks environments, including job health, Spark streaming workloads, structured streaming metrics, cluster behavior, failures, latency, throughput, and cost signals
• Help route Spark streaming metrics, operational logs, event-system signals, and platform health signals into monitoring tools such as Grafana
• Contribute to alerting patterns for Databricks workflows, Airflow/Astronomer DAGs, dbt jobs, data freshness, pipeline failures, event lag, dead-letter queues, and production data dependencies
• Contribute scripts and automation that reduce repetitive operational work and improve environment hygiene
• Support release and deployment reliability by validating changes, improving rollback readiness, and strengthening change safety
• Partner with data engineers, analytics engineers, and software engineers to improve reliability across pipelines, services, internal tools, event systems, and data products
• Participate in post-incident follow-up and help close corrective actions that prevent recurrence
• Support platform modernization and migration efforts, including orchestration platform changes, deployment system improvements, and shared reliability standards

Requirements:

• 5+ years of experience in SRE, DevOps, cloud infrastructure, platform engineering, software engineering, data platform operations, or related production-support roles
• Hands-on experience supporting Kubernetes-based workloads, deployment systems, cloud infrastructure, or production application environments
• Familiarity with Linux, HTTP, DNS, containers, Kubernetes, Git-based workflows, and scripting in Bash, Python, or similar languages
• Experience with monitoring, logs, metrics, dashboards, alerting, and incident management practices
• Comfort working with event systems such as Kafka, Google Pub/Sub, Kinesis, or similar technologies, including topics, subscriptions, consumers, retries, lag, and dead-letter queues
• Strong troubleshooting mindset, clear communication, and comfort operating in a production-support environment
• Interest in data platforms, data engineering systems, orchestration, streaming workloads, event-driven architecture, internal developer tools, and production data services
• Experience with Kubernetes deployment and release tooling such as Helm, ArgoCD, or similar GitOps workflows
• Experience with CI/CD automation using GitHub Actions, GitLab CI, Jenkins, or similar pipelines
• Familiarity with infrastructure as code using Terraform or similar provisioning tools
• Familiarity with Databricks, Spark, Spark Structured Streaming, Airflow, Astronomer, dbt, Kafka, Pub/Sub, object storage, or lakehouse architectures
• Experience with observability tools such as Grafana, Prometheus, Cloud Monitoring, Datadog, Splunk, or similar platforms
• Experience supporting internal applications, APIs, event-driven services, streaming consumers, or backend workers
• Exposure to data reliability concepts such as freshness, latency, completeness, pipeline health, data quality checks, and dependency-aware alerting
• Exposure to SLOs, SLAs, error budgets, postmortems, or formal reliability practices