This role pays less than
63%
of similar roles. Most pay
$126,800–$202,482
— the shaded band above.
At the midpoint, this role pays about
$150k
versus about
$165k
for comparable roles.
Based on 240 similar postings.
Employer
About Mastercard
Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services
Mastercard currently has
128 open roles
on FindRole.
Listed pay typically runs
$122,000–$207,000
across 114 roles with salary data.
As a Senior Data Engineer within Mastercard Foundry, you will take ownership of building and optimizing data platforms, pipelines, and systems to enable advanced analytics, machine learning, and business insights. You will design and maintain scalable data products, develop large-scale batch and streaming pipelines using distributed processing frameworks, and implement workflow orchestration for automated data movement. Your daily work involves integrating diverse data sources into high-quality datasets, managing data lakes, and defining scalable data models while ensuring compliance with security and governance requirements. The role requires proficiency in SQL, Microsoft SQL Server, Databricks, Spark, and cloud platforms like AWS, Azure, or GCP. You will solve complex technical challenges within the product area of emerging technologies, including digital assets, stablecoins, payment acceptance, processing, and applications of artificial intelligence to create revenue-generating ecosystems.
What does a Data Engineer earn?
Median $162000 from 204 postings across 57 companies.
Design, build, and maintain scalable data platforms and pipelines to support analytics and machine learning.
Develop and optimize large-scale batch and streaming data pipelines using distributed processing frameworks.
Implement workflow orchestration and automation solutions to ensure efficient data movement.
Integrate data from multiple sources to create high-quality datasets for business and technical stakeholders.
Build and maintain modern data lake and warehousing solutions to support diverse analytical use cases.
Define and implement scalable data models and database solutions to ensure performance and integrity.
Lead technical design discussions and contribute to architecture decisions across data platforms.
Troubleshoot production issues and perform root cause analysis to drive corrective actions.
What we're looking for
Bachelor's degree in a quantitative discipline such as Engineering, Mathematics, Finance, Business, or a related field (or equivalent practical experience).
Experience as a Data Engineer with strong knowledge of data engineering concepts, methodologies, and modern data architectures.
Strong hands-on experience with SQL for writing and optimizing queries for large-scale data processing.
Strong experience with relational databases, particularly Microsoft SQL Server and related technologies.
Experience designing, implementing, and maintaining ETL/ELT pipelines and data integration solutions.
Experience with distributed data processing and large-scale data platforms; experience with Databricks and Spark is a plus.
Experience building and maintaining data lakes, data models, and scalable database solutions for analytics and reporting.
Experience with event-driven or streaming architectures and exposure to machine learning or advanced analytics environments is a plus.
Artificial Intelligence
Machine Learning
Generative AI
Java
J2EE
Spring Boot
RESTful APIs
Microservices
SQL
CI/CD
Cloud-native
GitHub Copilot
Claude Code
Model Context Protocol
Distributed Systems
Site Reliability Engineering
Python
Go
Bash
Linux
Unix
AWS
Azure
GCP
CI/CD
Containerization
Orchestration
Observability
Monitoring
Troubleshooting
Capacity Planning
Network Administration
IT Service Management