Software Engineer, Data Platform (Foundations Lead)

Notion•San Francisco, CA

1d•$281,000 - $334,000•Hybrid

About The Position

Notion’s Data Foundations team builds and operates the batch and streaming infrastructure behind our product features, analytics, search, and AI experiences. We’re looking for a hands-on technical leader to shape the next generation of this platform as Notion serves larger customers, expands globally, and supports more data-intensive products. You’ll identify the highest-leverage problems, set direction, build and develop a high-performing team, and lead multi-quarter initiatives across our data lake, streaming, distributed-compute, governance, and reliability systems. You’ll stay close to critical technical decisions while creating clear ownership, growing engineers and technical leaders, and helping the team execute as one—partnering closely with Data Engineering, Data Product, Search, AI, Infrastructure, and Security. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days.

Requirements

Deep data-platform expertise. You have designed and operated large-scale batch and streaming systems and can reason across storage, compute, ingestion, orchestration, serving, governance, and production operations.
Direction-setting leadership. You have led a Data Platform or infrastructure team through multi-quarter work—finding the right problems, defining a strategy, sequencing execution, and driving decisions with long-term consequences.
Team-building and talent development. You have attracted strong engineers, grown people across career stages, developed technical leaders, and created clarity when expectations or performance needed to change.
Hands-on engineering judgment. You stay close to critical systems and designs and know when to prototype, simplify, buy, build, or migrate. You can go deep while preserving ownership for the engineers leading the work.
Platform-product thinking. You understand the needs behind partner requests and turn repeated patterns into safe, ergonomic, self-serve capabilities rather than accumulating bespoke solutions.
Organizational influence. You create clarity across team boundaries, surface uncomfortable technical or organizational risks early, and help engineers do their best work through strong context, delegation, feedback, and coaching.
Intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work.

Nice To Haves

AI fluency is a core requirement for some roles, but not explicitly stated as a requirement for this specific role, though intellectual curiosity and AI collaboration are expected.

Responsibilities

Set the direction for Data Foundations. Turn ambiguous company and customer needs into a clear multi-quarter strategy across the data lake, streaming, distributed compute, governance, and platform reliability—and align engineers and partner teams around it.
Build and develop a high-performing team. Attract strong engineers, set clear expectations, grow technical leaders, and create an environment of ownership, direct feedback, and accountability.
Lead the team’s hardest initiatives from idea to operation. Ensure complex work has strong technical ownership, de-risk architectures through prototypes, break work into shippable milestones, and coordinate execution across engineers and teams.
Make Notion’s data platform enterprise-ready. Guide the team toward durable foundations for external key management, data residency, access controls, lifecycle management, and governance without creating separate systems for every requirement.
Raise reliability, simplicity, and cost efficiency. Identify systemic risks and architectural gaps, establish clear service ownership, and lead migrations that make Kafka, Debezium, S3/Iceberg, Spark, EMR, Athena, and related systems easier to operate at scale.
Multiply the engineering organization. Establish reusable abstractions and paved paths, delegate meaningful ownership, raise the quality of technical decisions, and scale expertise through coaching, documentation, tooling, design reviews, and direct collaboration.