Notion · jobs.ashbyhq.com · checked today
Data Engineer, Product
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
- Data modelling
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 - Azure
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)
- Data quality
- 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
- Schema evolution & contracts
- 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)
- dbt
- 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)
- Dashboards & BI
- 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
Your plan
- The SQL screen: correct, then fast≈ 1 h
SQL · Data quality
- Average order value by countryFoundations
- Line items per orderFoundations
- Revenue by buyer countryFoundations
- Customers who never orderedFoundations
- Hiring funnel by roleFoundations
- Python: the data-wrangling round≈ 2 h
Python
- Clean spreadsheet headers into column namesFoundations
- Events normalization jobFoundations
- List the distinct composite keysFoundations
- Deduplicate rows, first one winsFoundations
- Remove repeats from a sorted list of partitionsFoundations
- Data modelling: the round most people fail≈ 2 h
Data modelling · dbt
- Marketplace core entitiesFoundations
- Cinema seat bookingFoundations
- City parking baysFoundations
- Dating app matchesFoundations
- Food delivery ordersFoundations
- Pipeline design: safe to run twice≈ 3 h
Airflow / orchestration · Schema evolution & contracts
- After the first one finishesFoundations
- Run it for last TuesdayFoundations
- The column that vanishedFoundations
- The history nobody keptFoundations
- The key that does not matchFoundations
- Spark: read the plan Spark actually ran≈ 1 h
Spark
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- countDistinct vs approx_count_distinct: what the extra shuffle buysFoundations
- COUNT(*) vs COUNT(column): the null trapFoundations
- Grouping by two columns: what changes in the shuffle?Foundations
- Does the join type change the join strategy?Foundations
- Say it out loud≈ 1 h
25 drills · Foundations + Intermediate≈ 10 hours
Not covered by the plan: AWS, Azure, GCP, Dashboards & BI, Java, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 4 dremoved the moment Notion closes it