Leader, Software Engineering Data Platform | Microservices | Cloud

Job Description

Meet the TeamWebex Data Analytics Platform (WAP) engineering organization is responsible for developing the big data platform and core services at Webex. The platform and microservice ecosystem form the base on which other teams build analytics, data pipelines, and product capabilities.We are looking for a Manager, Software Engineering, who will lead technical strategy and delivery for our platform and engineering teams. You will own program and project delivery, drive microservice and data-platform evolution, strengthen engineering processes, and champion AI adoptionworking closely with engineering, product, and leadership to deliver high-impact outcomes on time and within scope. You will bring structure to ambiguity, visibility to complexity, and ensure our distributed teams operate efficiently and successfully.Your ImpactLead end-to-end program and project delivery for sophisticated, cross-functional initiatives spanning microservice architecture, data platform, and engineering excellence.Define technical roadmaps, scope, objectives, success criteria, and key turning points in collaboration with engineering and product leadership.Own and evolve microservice architecture standards, API develop, service boundaries, and operational practices across the organization.Drive data platform strategy and execution: pipelines, storage, analytics, and ML infrastructure in partnership with data and platform teams.Establish and improve engineering processes (develop reviews, code quality, testing, CI/CD, incident response) and supervise KPIs for program health and delivery.Champion AI adoption and build high-performing engineering teams; identify use cases, prioritize initiatives, and foster a culture of continuous improvement.Minimum QualificationsAt least 12 years of experience in software engineering, with 4 years in engineering leadership, managing Software Engineering or Data Engineering teams.Strong track record of delivering large-scale, multi-team programs in microservice and/or data-heavy environments; deep experience with microservice architecture, cloud-native development, data platform and distributed systems.Strong oversight experience with data platforms: data pipelines, warehouses/lakes, streaming, and analytics/ML infrastructure.Experience defining and rolling out engineering processes (Agile/Scrum, compose reviews, CI/CD, SRE practices) and driving AI/ML adoption; proficiency with OKRs, KPIs, and roadmaps.Proficiency with program management tools such as Jira, Confluence, Asana, Smartsheet, or Microsoft Project.Preferred QualificationsAbility to translate business and product requirements into technical specifications and actionable engineering roadmaps.Experience in regulated or high-availability domains (e.g. teamwork, fintech, healthcare, enterprise SaaS).Familiarity with modern data stack (e.g., Spark, Kafka, dbt, Snowflake/Databricks), ML platforms, and DORA-style metrics.Familiarity using AI tools (e.g., Copilot, Cursor, Codex) for efficiency and team adoption.Why Cisco At Cisco, were revolutionizing how data and infrastructure connect and protect organizations in the AI era and beyond. Weve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and youll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you.

Keyskills :
Software EngineeringMicroservicesCloudEngineering ManagementData PlatformAI adoptionMicroservice architectureCloudnative developmentData pipelines