
Senior Manager - Data Platform AI Enablement
- San Jose, CA
- Permanent
- Full-time
- Leadership & Strategy
- Define and drive the vision and roadmap for enterprise data platforms supporting analytics and AI.
- Manage and mentor a high-performing systems engineering team specializing in big data technologies.
- Establish best practices for scalable data processing, governance, and performance optimization.
- Platform & Systems Engineering
- Lead the architecture, deployment, and operation of data platforms using Apache Iceberg, Apache Spark, Kafka or Flink, Nessie/Unity/Polaris Catalogue, and cloud-native equivalents.
- Ensure efficient data ingestion, storage, and processing for structured, semi-structured, and unstructured data.
- Oversee the integration of streaming, batch, and real-time pipelines supporting AI/ML workloads.
- AI & Analytics Enablement
- Partner with data science and AI teams to ensure platforms support advanced model training, inference, and data exploration.
- Drive the adoption of modern data Lakehouse architectures and query engines.
- Enable self-service analytics and AI experimentation through robust data platforms.
- Operational Excellence
- Define and monitor KPIs for reliability, scalability, and cost efficiency of data systems.
- Implement security, compliance, and governance practices across the data ecosystem.
- Champion automation in deployment, monitoring, and platform lifecycle management.
- Extensive experience in systems engineering or data engineering, including leadership roles.
- Proven expertise in big data technologies: Apache Iceberg, Spark, Kafka, Hadoop ecosystem, and modern lakehouse/data warehouse platforms.
- Strong understanding of distributed systems, data storage, streaming, and parallel processing frameworks.
- Experience enabling AI/ML workflows through data platforms (training data pipelines, feature stores, inference pipelines).
- Solid track record in leading teams, managing large-scale systems, and driving enterprise-wide platform adoption.
- Familiarity with cloud-native data platforms (AWS EMR, Glue, GCP Dataproc, Dremio, Presto/Trino, BigData, Databricks, Snowflake) is a plus.
- Excellent communication, stakeholder management, and cross-functional collaboration skills.
- Hands-on knowledge of containerization (Kubernetes, Docker) and infrastructure-as-code.
- Experience with data governance, lineage, and cataloging tools.
- Knowledge of observability, data quality frameworks, and AI pipelines
- Familiarity with AI-specific platforms (MLflow, Ray, TensorFlow Extended, Feast).
- Bachelor’s or Master’s in Computer Science, Engineering, or related field.