Director of Engineering overseeing engineering infrastructure at Adobe. Leading teams, embedding AI in processes, and scaling to millions of users in a collaborative environment.
Responsibilities
Provide strategic direction and execution for Production delivery, Developer Tools, Observability and associated engineering environments.
Embed AI into every facet of the engineering lifecycle, from internal solutions to vendor integrations to elevate efficiency and customer experience.
Scale platforms to support millions of users and dozens of tenants, ensuring performance and reliability at enterprise scale.
Lead and grow a distributed engineering organization of 50+ professionals.
Define and implement the strategy for building a self-service, enterprise-scale reliability platform.
Foster a culture of collaboration, experimentation, and rapid iteration across product, engineering, and marketing teams, enabling fast prototyping and innovation with a “fail forward” approach.
Establish and enforce standards around frameworks, technologies, and processes to ensure consistency and simplicity across services.
Define, measure, and continuously improve engineering productivity by setting benchmark critical metrics and SLAs, demonstrating data-driven insights for optimization.
Requirements
Demonstrated use of AI/ML to enhance engineering productivity and organizational efficiency.
Proven leadership in high-growth environments, with experience running enterprise-scale services across private and public cloud platforms.
A strong track record of leading large, geo-distributed engineering organizations.
Expertise in designing and managing complex development frameworks, deployment tools, test environments, and automation systems.
Experience building or demonstrating A/B testing infrastructure and applying SRE operating models.
Deep technical expertise with at least one major public cloud (Azure, AWS) and open-source technologies such as Kubernetes, Istio, Prometheus, Elasticsearch, Kafka, and Spark.
Strong understanding of deployment systems, CI/CD pipelines, and system configuration.
Experience delivering integrated end-to-end systems, including source control, build tools, artifact repositories, deployment, and monitoring.
Proficiency in performance engineering and load testing at scale.
Ability to define and manage SLAs for production-scale systems, ensuring reliability and data quality across platforms.
Proven success in delivering high-impact dashboards, datasets, and visualizations to drive business and product decisions.
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