Senior Data Engineer
About The Position
Mastercard’s Data Collection & Engineering (DC&E) organization is seeking a Senior Data Engineer to play a critical role in designing, building, and scaling Mastercard's next-generation data platforms and analytical ecosystems. In this role, you will develop high-performance, cloud-enabled data pipelines that power Mastercard's enterprise data warehouse and Lakehouse environments. Your work will enable advanced analytics, business intelligence, regulatory reporting, and data-driven decision making across the organization. This role offers a unique opportunity to solve large-scale data engineering challenges while working with cutting-edge big data technologies and contribute to a modern cloud transformation program supporting global payment processing platforms. The Data Collection & Engineering (DC&E) organization powers the global economy by enabling secure, seamless, and intelligent payments across the world. Behind every transaction is a sophisticated technology ecosystem that processes billions of payment events with speed, resilience, and precision. This is a hybrid position based in O’Fallon, MO, requiring three days per week onsite.
Requirements
- Hands-on experience as a Data Engineer or Senior Data Engineer delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
- Proven experience delivering multiple end-to-end data engineering initiatives within large-scale distributed computing environments.
- Hands-on experience migrating ETL/ELT, data warehouse, and analytics workloads from on-premises platforms to cloud-native architectures.
- Strong development experience with Apache Spark, Scala and/or Java, Hadoop ecosystem technologies, and cloud object storage platforms.
- Experience building orchestration and workflow solutions using Apache Airflow, Apache NiFi, or comparable enterprise scheduling and workflow frameworks.
- Strong SQL expertise and experience working with relational and NoSQL databases, such as Oracle, SQL Server, Cassandra, and DynamoDB.
- Working knowledge of cloud platforms, preferably AWS, including Amazon S3, EMR, AWS Glue, and other cloud-native data services.
- Strong understanding of security, privacy, regulatory, and compliance requirements associated with sensitive financial and customer data.
- Proven ability to lead complex technical initiatives across multiple teams and influence engineering direction without direct authority.
- Demonstrated ability to mentor engineers and elevate technical capability, including providing design guidance, knowledge sharing, and constructive feedback.
- Excellent communication and stakeholder management skills, with the ability to clearly articulate technical concepts, trade-offs, risks, dependencies, and recommendations to both technical and business audiences.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline. Equivalent practical experience will also be considered.
Responsibilities
- Design, develop, test, and deploy secure, scalable, high-performance, and resilient data pipelines using Apache Spark, Java/Scala, Hadoop, and cloud-native object storage platforms.
- Build and maintain batch and near-real-time data processing frameworks capable of supporting petabyte-scale workloads and demanding enterprise data requirements.
- Develop reusable engineering components, frameworks, and design patterns that accelerate data product delivery while maintaining enterprise architecture and engineering standards.
- Design and implement “build once, run anywhere” architectures that enable seamless deployment across on-premises and public cloud environments without requiring code changes.
- Implement enterprise data capabilities including data lineage, metadata management, data cataloging, data quality, monitoring, and observability across the data ecosystem.
- Collaborate with architects and platform teams to establish scalable architecture patterns, distributed computing practices, and engineering standards, while driving adoption of modern Lakehouse architectures.
- Contribute to cloud modernization initiatives by migrating legacy ETL, data warehouse, and analytics workloads from on-premises environments to cloud-native architectures using services such as Amazon S3, EMR, and AWS Glue.
- Lead end-to-end engineering activities, including requirements analysis, solution design, coding, testing, deployment, production support, and continuous optimization.
- Partner with product owners, analysts, architects, and business stakeholders to translate requirements into high-quality, scalable solutions and deliver committed outcomes within established timelines.
- Troubleshoot complex production incidents, perform root cause analysis, and implement sustainable remediation strategies while ensuring compliance with Mastercard’s security, quality, and operational governance standards.
- Mentor and guide engineers through code reviews, technical coaching, knowledge sharing, and best practices, while identifying opportunities to improve performance, automation, monitoring, and engineering efficiency.
Benefits
- insurance (including medical, prescription drug, dental, vision, disability, life insurance)
- flexible spending account and health savings account
- 16 weeks of new parent leave
- up to 20 days of bereavement leave
- 80 hours of Paid Sick and Safe Time
- 25 days of vacation time
- 5 personal days
- 10 annual paid U.S. observed holidays
- 401k with a best-in-class company match
- deferred compensation for eligible roles
- fitness reimbursement or on-site fitness facilities
- eligibility for tuition reimbursement
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What This Job Offers
Job Type
Full-time
Career Level
Senior
