Salesforce · salesforce.wd12.myworkdayjobs.com · checked today
Senior Data Engineer
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Skills, with evidence
- Airflow / orchestration
You have hands-on experience with large-scale data technologies and platforms such as Snowflake, Spark, Airflow, and Hive.
must have - Data modelling
You have a proven track record of architecting and optimizing data models, schemas, and processing workflows to improve performance, scalability, cost efficiency, and reliability in modern data warehouse environments.
must have - Data quality
You champion data quality, governance, and reliability while designing and scaling data models, pipelines, and metric systems that provide consistent and timely access to business insights.
must have - Python
You are proficient in at least one programming language commonly used in Data Engineering, such as Python or Java.
must have - SQL
You have deep expertise in SQL and proven experience designing scalable data pipelines and data transformations that operate reliably across large and complex datasets.
must have - Spark
You have hands-on experience with large-scale data technologies and platforms such as Snowflake, Spark, Airflow, and Hive.
must have - Governance & security
We partner with Product, Data Science, Analytics, and Engineering teams to define and manage critical product and business metrics through centralized governance, ensuring high data quality, consistency, and adherence to targeted SLAs (service-level agreements).
not practised here - Cost & performance
You design, build, and optimize data pipelines that transform billions of records into trusted, actionable datasets and metrics.
- Warehousing
You have a proven track record of architecting and optimizing data models, schemas, and processing workflows to improve performance, scalability, cost efficiency, and reliability in modern data warehouse environments
- Java
You are proficient in at least one programming language commonly used in Data Engineering, such as Python or Java
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
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling · Warehousing
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration
- 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
25 drills · Intermediate + Advanced≈ 14 hours
Not covered by the plan: Governance & security, Java.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 2 dremoved the moment Salesforce closes it