Lead Data Engineer With MarTechLocation: SFO, CA (Hybrid 2 days a week)Key ResponsibilitiesLead end-to-end MarTech engineering initiatives across orchestration, data processing, and activation pipelines.Architect scalable, event-driven systems that power real-time marketing experiences and automated customer journeys.Design and implement orchestration workflows using Adobe Campaign or equivalent enterprise-grade tools. Develop high-performance big-data applications using Scala, Databricks, Spark SQL, Spark Streaming, and Python. Build and optimize cloud-native data pipelines on Azure, including ADF-based ingestion, transformation, and orchestration.Apply modern design patterns to ensure reliability, maintainability, and scalability across distributed systems.Drive AI-assisted engineering practices including Vibe Coding and other generative-AI development accelerators.Collaborate with product, marketing, and data teams to translate business needs into robust technical solutions.Mentor engineers and elevate engineering standards, code quality, and operational excellence within the POD.Required Skills & ExperienceDeep expertise in MarTech platforms with hands-on experience in Adobe Campaign or similar orchestration tools.Strong proficiency in big-data technologies: Scala, Databricks, Spark SQL, Spark Streaming, Python.Cloud engineering experience with Azure services, including Azure Data Factory.
Advanced system design capabilities including event-driven architectures and distributed design patterns.Experience with AI-augmented development such as Vibe Coding or comparable frameworks.Proven ability to lead engineering teams in a fast-paced, cross-functional environment.Strong communication and stakeholder alignment skills with the ability to translate technical concepts into business impact.Preferred Qualifications Experience in large-scale marketing ecosystems (ESP, CDP, personalization engines, real-time decisioning).Background in high-volume data processing supporting customer engagement or growth marketing. Familiarity with DevOps practices including CI/CD, observability, and automated testing.Exposure to modern AI/ML pipelines for personalization, segmentation, or content automation.