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Salary: USD 281,000 - 334,000 / annual
Notion is seeking a hands-on technical leader to lead the Data Foundations team, which builds and operates batch and streaming infrastructure powering product features, analytics, search, and AI experiences. This is a director-level role managing a high-performing engineering team while staying deeply involved in critical technical decisions.
Key Responsibilities:
- Set multi-quarter strategy for the data platform across data lake, streaming, distributed compute, governance, and reliability systems
- Build and develop a high-performing team: attract strong engineers, grow technical leaders, establish clear ownership and accountability
- Lead complex initiatives from conception through operation, ensuring strong technical ownership and coordinated execution across teams
- Make Notion's data platform enterprise-ready with external key management, data residency, access controls, lifecycle management, and governance
- Improve reliability, simplicity, and cost efficiency across Kafka, Debezium, S3/Iceberg, Spark, EMR, Athena, and related systems
- Establish reusable abstractions and paved paths to scale expertise across the engineering organization
- Partner closely with Data Engineering, Data Product, Search, AI, Infrastructure, and Security teams
Problems You May Lead:
- Evolving Notion's data lake toward S3 + Iceberg lakehouse architecture with stronger governance and lifecycle controls
- Scaling Kafka and Debezium-based streaming infrastructure for real-time product, analytics, and AI use cases
- Improving Spark and distributed-compute performance, workload isolation, and cost efficiency
- Designing external key management, data residency, and compliant processing capabilities
- Creating platform interfaces and self-serve primitives for downstream teams
Work Environment: Hybrid with anchor days (Monday, Tuesday, Thursday) in office. Role can be based in San Francisco or New York City.
Requirements:
- Deep data-platform expertise: designed and operated large-scale batch and streaming systems; can reason across storage, compute, ingestion, orchestration, serving, governance, and production operations
- Direction-setting leadership: led a Data Platform or infrastructure team through multi-quarter initiatives; skilled at problem identification, strategy definition, execution sequencing, and long-term decision-making
- Team-building and talent development: attracted strong engineers, grown people across career stages, developed technical leaders, managed performance and expectations
- Hands-on engineering judgment: stays close to critical systems and designs; knows when to prototype, simplify, buy, build, or migrate while preserving engineer ownership
- Platform-product thinking: understands partner needs and converts repeated patterns into safe, ergonomic, self-serve capabilities
- Organizational influence: creates clarity across team boundaries, surfaces technical and organizational risks early, enables engineers through context, delegation, feedback, and coaching
- Intellectual curiosity and AI fluency: excited to use AI as a real collaborator in work; treats AI as a tool to think better and move faster, not a novelty
