Coupang · coupang.jobs · checked today
Data Engineering Lv.4
We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did I ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.
Skills, with evidence
- Cost & performance
Optimize systems for performance, cost efficiency, and scalability.
must have - Data modelling
Proficiency in Data modelling, SQL and Python.
must have - Data quality
Ensure data quality, observability, and reliability across the data lifecycle.
must have - Python
Proficiency in Data modelling, SQL and Python.
must have - SQL
Proficiency in Data modelling, SQL and Python.
must have - Airflow / orchestration
Hands-on experience with version control (GitHub), workflow orchestration, and ETL scheduling tools (e.g. Airflow, Dagster, DBT).
- dbt
Hands-on experience with version control (GitHub), workflow orchestration, and ETL scheduling tools (e.g. Airflow, Dagster, DBT).
- Dashboards & BI
Create and maintain dashboards and reporting infrastructure to surface key PPE metrics to stakeholders.
not practised here - AWS
Experience with distributed storage systems (e.g., HDFS, S3).
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
Data quality · SQL
- 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
- After the first one finishesFoundations
- Run it for last TuesdayFoundations
- The history nobody keptFoundations
- The spreadsheet is a dependencyFoundations
- Too slow by morningFoundations
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- partitionBy("country"): what does partition pruning look like in the plan?Foundations
- Predicate pushdown: equality, ranges, and the filter that cannot be pushedFoundations
- Revenue by country: read the DAG behind one groupByFoundations
- Too many small files: where do they cost you, and how does compaction help?Foundations
- Say it out loud≈ 1 h
Not covered by the plan: Dashboards & BI, AWS.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 9 dremoved the moment Coupang closes it