Wise · api.smartrecruiters.com · checked today
Senior Data Engineer II - Scalable Growth
Wise is a global technology company, building the best way to move and manage the world’s money. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money.
Skills, with evidence
- Airflow / orchestration
Hands-on experience using dbt for scalable data transformations and Airflow for workflow orchestration.
must have - Data quality
A strong background in designing scalable, fault-tolerant data architectures, implementing data quality frameworks, and establishing production best practices.
must have - Python
Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines (e.g., Kafka Streams, Event Streaming, Trino, Iceberg).
must have - Streaming
Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines (e.g., Kafka Streams, Event Streaming, Trino, Iceberg).
must have - dbt
Hands-on experience using dbt for scalable data transformations and Airflow for workflow orchestration.
must have - Failure handling
Establish best practices for monitoring, reliability, and scale across our data ecosystem (using Python, dbt, Airflow, Kafka, and Trino/Iceberg).
- SQL
Establish best practices for monitoring, reliability, and scale across our data ecosystem (using Python, dbt, Airflow, Kafka, and Trino/Iceberg).
- Cost & performance
Scalable Growth is dedicated to building enabling technology that helps Wise acquire customers at the lowest possible cost.
- Warehousing
Python & Big Data Expertise: Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines (e.g., Kafka Streams, Event Streaming, Trino, Iceberg).
Your plan
- The SQL screen: correct, then fast≈ 2 h
Data quality · SQL
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
dbt · Warehousing
- Addresses that stay true to the pastIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Chats, members and read receiptsIntermediate
- Churn that survives an argumentIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Streaming · Failure handling
- The source will not let youIntermediate
- Parcel tracking pipelineIntermediate
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- Five minutes behind the sourceIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- A filter after a window function: pushed down or not?Advanced
- Say it out loud≈ 1 h
25 drills · Intermediate + Advanced≈ 14 hours
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 9 dremoved the moment Wise closes it