Mastercard · mastercard.wd1.myworkdayjobs.com · checked today
Senior Data Engineer
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible.
Skills, with evidence
- Airflow / orchestration
Experience with orchestration and workflow automation tools (e.g., Airflow).
must have - Data quality
Understanding of data governance, security controls, data quality, metadata, and lineage.
must have - Python
Strong proficiency in Python, SQL, Spark/PySpark, and data engineering best practices.
must have - SQL
Strong proficiency in Python, SQL, Spark/PySpark, and data engineering best practices.
must have - Spark
Strong proficiency in Python, SQL, Spark/PySpark, and data engineering best practices.
must have - AWS
Hands-on experience with Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS and/or Azure.
must have · not practised here - Streaming
Build and operate scalable batch and streaming data pipelines.
- Warehousing
The platform leverages Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS/Azure to enable enterprise data sharing, analytics, AI/ML, and data commercialization capabilities.
- Governance & security
Implement data governance, security, observability, and operational controls.
not practised here - Terraform
Support platform modernization, automation, and engineering excellence through CI/CD and Infrastructure as Code.
not practised here - Schema evolution & contracts
Experience with Data Contracts, Data Mesh, and data product architectures.
- Azure
Hands-on experience with Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS and/or Azure.
not practised here - GCP
Hands-on experience with Databricks, Snowflake, Apache Iceberg, Delta Lake, and AWS and/or Azure.
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
Warehousing
- Subscription warehouse grainIntermediate
- Addresses that stay true to the pastIntermediate
- Campaign efficiencyIntermediate
- Chats, members and read receiptsIntermediate
- Churn that survives an argumentIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Streaming · Schema evolution & contracts
- Is this change safe?Intermediate
- The source will not let youIntermediate
- The table everyone depends onIntermediate
- Parcel tracking pipelineIntermediate
- Marketplace transactions at scaleIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Spark
- 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: AWS, Governance & security, Terraform, Azure, GCP.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Mastercard closes it