Senior Director - Data Platform and Cloud Services

About the Role

What You'll Do

• Set the Vision: Define and execute a transformative vision for a data platform that scales to billions of daily events, partnering with the CTO and engineering leadership to align strategy with business objectives.
• Evolve the Architecture: Lead the evolution to a modern, cloud-native data platform with embedded ML and AI, delivering fault-tolerant, multi-region architectures for real-time and batch processing and evaluating emerging technologies.
• Build Platform Services: Build and maintain the services, platforms, and frameworks that enable Samba's data engineering teams, driving automation and self-service capabilities for internal customers.
• Architect Embedding Systems: Architect embedding-based systems for content understanding, viewer similarity, semantic search, and real-time recommendations, with infrastructure for multimodal embeddings and vector storage, indexing, and retrieval at billions of embeddings.
• Enable Self-Service ML: Enable self-service ML for data scientists and analysts, establish MLOps practices for the embedding model lifecycle, and champion best practices in data governance, security, and compliance (GDPR, CCPA).
• Build the Team: Build and mentor a world-class team of platform engineers, ML engineers, and architects, fostering innovation and continuous learning, establishing clear career paths, and driving cross-functional collaboration.
• Run Operations: Establish SLAs and monitoring for internal and external clients, provide observability and data quality tools for Data Engineering and Data Science, and lead vendor relationships and technology partnerships.

What You Bring

• 12+ years in data platform and infrastructure roles, with 5+ years in senior leadership, and a history of building and scaling teams of 20+ engineers.
• A proven track record of leading large-scale platform transformations and of influencing without authority across organizational boundaries.
• Experience building production embedding systems and vector databases, with deep expertise in modern data architectures (data mesh, lakehouse, streaming-first).
• Proficiency with stream processing (Kafka, Flink, Spark Streaming), modern data warehouses and lakes (Snowflake, Databricks, BigQuery), workflow management (Airflow), container orchestration (Kubernetes, EKS, GKE), Infrastructure as Code (Terraform, CloudFormation), and ML platforms (MLflow, Kubeflow, SageMaker).
• Exceptional communication skills for explaining complex technical concepts to diverse audiences, strategic thinking that balances long-term vision with short-term deliverables, and strong business acumen connecting technical decisions to business outcomes.
• Knowledge of privacy-preserving analytics and clean room technologies, and contributions to open-source projects in ML infrastructure or vector search.
• An advanced degree in Computer Science, Machine Learning, or a related field, or equivalent experience.