Mastercard · mastercard.wd1.myworkdayjobs.com · checked today
Lead Data Engineer (Data Platforms)
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
- AWS
Design and develop cloud-native data and BI solutions using Databricks and Snowflake on AWS.
must have · not practised here - Azure
Drive implementation of multi-cloud architecture leveraging AWS and Azure (Azure Databricks, ADLS Gen2).
must have · not practised here - Spark
This role focuses on delivering Business Intelligence (BI), data engineering, and platform capabilities using Databricks, Snowflake, AWS, and Azure, while ensuring strong governance, scalability, and interoperability across multi-cloud environments.
- Governance & security
This role focuses on delivering Business Intelligence (BI), data engineering, and platform capabilities using Databricks, Snowflake, AWS, and Azure, while ensuring strong governance, scalability, and interoperability across multi-cloud environments.
not practised here - Cost & performance
Understanding of FinOps, cost optimization, and platform observability
- Data quality
Understanding of FinOps, cost optimization, and platform observability
- Warehousing
Knowledge of open data formats (Delta Lake, Apache Iceberg)
- Terraform
Experience with CI/CD, Infrastructure as Code, and automation
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
Data quality
- Interview audit union capstoneAdvanced
- Airbnb: converting on a day with no published rateAdvanced
- Current-row snapshot auditAdvanced
- Customer null completenessAdvanced
- Data-quality audit summaryAdvanced
- Data modelling: the round most people fail≈ 4 h
Warehousing
- Spark: read the plan Spark actually ran≈ 2 h
Spark · 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
- A correlated COUNT(*) subquery: the join Spark runs, and the count bug it avoidsAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- Say it out loud≈ 1 h
15 drills · Advanced + Intermediate≈ 9 hours
Not covered by the plan: AWS, Azure, Governance & security, Terraform.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 13 dremoved the moment Mastercard closes it