Data Engineer, Product Mid

D8 LooP; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

Notion is seeking a Mid-level to Senior Data Engineer in San Francisco to design and optimize data pipelines and analytics tooling. You will work with a stack including Python, SQL, Airflow, DBT, Spark and Hive to deliver scalable data products.

Strong knowledge of cloud data platforms (AWS, GCP, Azure) is essential, with a focus on performance, reliability, and self-serve data access. The role emphasizes building automated workflows, SLAs for data delivery, and democratizing data for teams via

Qualifications

• Analytical mindset and passion for data analytics, data modeling, and solving complex problems.
• SQL expert with advanced aggregation, window functions, UDFs, self-joins, and optimization techniques.
• Experience with scalable cloud data solutions across AWS, GCP and Azure.
• Hands-on design and building of scalable data pipelines using Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift.

Responsibilities

Skills

Python

SQL

AWS

GCP

Spark

Airflow

DBT

Hive

Protobuf/Thrift

Parquet/ORC

Job description

Notion · jobs.ashbyhq.com · checked today

San Francisco, California Mid 5+ yrs Data Engineer posted 2 d ago

Notion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.

Skills, with evidence

with a passion for analytics, data modeling, and solving complex problems must have

• Python Highly comfortable with object-oriented programming (Python, Java, Scala) must have
• SQL SQL expert, proficient with aggregation, window functions, UDFs, self-joins, and optimization techniques for writing correct, high-performance queries must have
• AWS Strong understanding of scalable cloud data solutions (AWS, GCP, Azure) across multiple data stores must have · not practised here
• Strong understanding of scalable cloud data solutions (AWS, GCP, Azure) across multiple data stores must have · not practised here
• GCP Strong understanding of scalable cloud data solutions (AWS, GCP, Azure) across multiple data stores must have · not practised here
• Airflow / orchestration Hands-on experience designing and building highly scalable, reliable data pipelines using BigData stack tools (e.g., Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift)
• - Implement automated workflows that reduce operational costs, define SLAs for timely data delivery, and democratize data through self-serve dashboards, discovery tools, and AI-enabled exploration
• - Hands-on experience designing and building highly scalable, reliable data pipelines using BigData stack tools (e.g., Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift)
• Spark - Hands-on experience designing and building highly scalable, reliable data pipelines using BigData stack tools (e.g., Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift)
• - Implement automated workflows that reduce operational costs, define SLAs for timely data delivery, and democratize data through self-serve dashboards, discovery tools, and AI-enabled exploration not practised here
• Java - Highly comfortable with object-oriented programming (Python, Java, Scala) not practised here
• Scala - Highly comfortable with object-oriented programming (Python, Java, Scala) not practised here