• You are a self-starter and continuously gather and synthesize high-impact needs from business partners, designing and implementing the appropriate technical solutions, and effectively communicating about deliverables, timelines and tradeoffs
• You've spent 4+ years as a data engineer building core datasets and supporting business verticals as needed. You are passionate about analytics use cases, data models and solving complex data problems
• You have hands-on experience shipping scalable data solutions in the cloud (e.g AWS, GCP, Azure), across multiple data stores (e.g Snowflake, Redshift, Hive, SQL/NoSQL, columnar storage formats) and methodologies (e.g dimensional modeling, data marts, star/snowflake schemas)
• You are a SQL expert. You intimately understand aggregation functions, window functions, UDFs, self-joins, partitioning and clustering approaches to run correct and highly-performant queries
• You are highly comfortable with object-oriented programming paradigms (e.g Python, Java, Scala)

Desirable

• As Notion continues to grow rapidly, we're seeking talented data engineers to join our team and help us build foundational datasets and pipelines, as well as the infrastructure that supports them
• Our work will accelerate the decision-making process of key product and business functions
• If you're passionate about analytics use cases, data models, and solving complex data problems, then this is the team for you!
• You'll define the processes and ETL infrastructure to transform and make data readily available across the company
• You'll build core datasets to serve as unique sources of truth for product and business functions (product, marketing, sales, finance, customer experience, data science, business operations, IT, engineering)
• You'll partner with data scientists and other internal stakeholders to understand their needs and then design, build and monitor pipelines that meet today's requirements but can gracefully scale with our growing data size
• You'll implement automated workflows that lower manual/operational cost for stakeholders, define and uphold SLAs for timely delivery of data, move the company closer to democratizing data and a self-serve model (query exploration, dashboards, data catalog, data discovery)