Zoom · zoom.wd5.myworkdayjobs.com · checked today
Senior Data Engineer (Online Data)
In this role, you will design, build, and optimize the data pipelines and analytical solutions that power Zoom’s Online business, including billing, checkout, growth, and revenue analytics. Working end to end, you will own the full data engineering lifecycle, from ETL design and ingestion through data quality, performance tuning, and model delivery to stakeholders across Data Science, Finance, and Product. Each day brings a mix of deep technical work and meaningful cross-functional collaboration, with opportunities to shape how the Online business understands and acts on its data.
Skills, with evidence
- Airflow / orchestration
Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python.
must have - Data quality
Implement automated data quality frameworks covering accuracy, completeness, consistency, and freshness, and establish alerting to identify and resolve data issues proactively.
must have - Python
Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python.
must have - SQL
SQL and database mastery: Proficiency designing normalized schemas, optimizing complex queries, and applying advanced database features for high-performance analytical workloads.
must have - Spark
Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python.
must have - dbt
Data engineering expertise: 8+ years of hands-on experience designing and optimizing complex data pipelines and ETL workflows, including error handling, monitoring, and performance tuning, using tools such as Snowflake, Databricks, Spark, Airflow, dbt, and Python.
must have - Machine learning
Machine learning and statistical methods: Experience developing and optimizing ML solutions, designing and executing hypothesis testing strategies, and applying statistical techniques to validate analytical approaches.
must have · not practised here - Cost & performance
In this role, you will design, build, and optimize the data pipelines and analytical solutions that power Zoom’s Online business, including billing, checkout, growth, and revenue analytics.
- Failure handling
Design and implement scalable ETL workflows and data pipelines that transform raw data into structured, analytics-ready formats, with automated validation checks and monitoring to ensure ongoing quality and reliability.
- Governance & security
Establish and maintain data governance practices, including data definitions, lineage, ownership documentation, and access controls for sensitive data, in collaboration with cross-functional stakeholders.
not practised here
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
- Keep only the latest row for each eventIntermediate
- Pivot a long metrics table to wideIntermediate
- Data modelling: the round most people fail≈ 1 h
dbt
- Addresses that stay true to the pastIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Failure handling
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- The source will not let youIntermediate
- Changing a pipeline that’s already runningIntermediate
- SLA-aware alerting flowIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Spark · Cost & performance
- broadcast() with auto-broadcast off, and the case where Spark ignores itIntermediate
- autoBroadcastJoinThreshold compares an estimate: flip a join with select() and one settingIntermediate
- Does Spark really run your EXISTS subquery once per row?Intermediate
- left_semi and left_anti: "customers who did / never did" without a full joinIntermediate
- A self-join on a real key that still multiplies rowsIntermediate
- Say it out loud≈ 1 h
21 drills · Intermediate + Advanced≈ 11 hours
Not covered by the plan: Machine learning, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Zoom closes it