Senior Data Engineer

Title and Summary

Senior Data Engineer

Overview:
We are seeking a Senior Data Engineer with expertise in Apache Spark, Apache Iceberg, Apache Airflow, and AWS to design and build the next generation of our finance data platform. You will own finance-focused data products while contributing to foundational platform capabilities and engineering standards that scale across the enterprise.

• Design, develop, and maintain data products
• Ensure data products meet quality, auditability, lineage, and compliance standards

• Build reusable data engineering frameworks and accelerators used across multiple teams
• Develop standardized patterns for ingestion, transformation, orchestration, monitoring, and data quality
• Contribute to and promote engineering standards and best practices across the team for Spark, Iceberg, Airflow, and cloud-native engineering
• Drive adoption of self-service platform capabilities and common engineering standards
• Build and optimize Apache Iceberg-based lakehouse solutions for analytical and operational workloads
• Design and optimize distributed processing workloads to ensure performance, resiliency, scalability, and cost efficiency
• Design, implement, and support workflow orchestration solutions that manage dependencies, scheduling, monitoring, and recovery across complex data pipelines
• Integrate and transform data from multiple internal and external sources to create trusted, reusable, and business-ready datasets
• Design and maintain logical and physical data models that support scalable analytics, reporting, and data product development
• Apply data security and governance standards including access controls, encryption, data masking, regulatory compliance, and secure data lifecycle management

• Build cloud-native solutions on AWS – S3, EMR, Glue, Lambda, ECS/EKS, Cloud Watch
• Implement CI/CD pipelines, automated testing, and Infrastructure-as-Code

• 4+ years of experience in data engineering, data platform development, or related technical roles
• Experience designing and implementing scalable data platforms and data products in enterprise environments
• Knowledge of data mesh or data product architectures in enterprise settings
• Deep hands-on experience with Apache Spark (PySpark) for large-scale data processing and pipeline development
• Deep hands-on experience with Apache Iceberg or similar open table formats
• Solid understanding of CI/CD pipelines, infrastructure-as-code, and Dev Ops practices
• Experience with data governance, data quality frameworks, and metadata management tools
• Strong experience with AWS Cloud services, including Amazon S3, EMR, Glue, Lambda, ECS/EKS, and Cloud Watch for developing, deploying, and managing cloud-native data solutions

• Abide by Mastercard’s security policies and practices
• Ensure the confidentiality and integrity of the information being accessed
• Report any suspected information security violation or breach
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines

Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

Insurance (medical, prescription drug, dental, vision, disability, life), flexible spending account, health savings account, paid leaves (including 16 weeks new parent leave, up to 20 days bereavement leave, 80 hours Paid Sick and Safe Time, 25 days vacation and 5 personal days, pro-rated), 10 annual paid U.S. observed holidays, 401(k) with company match, deferred compensation, fitness reimbursement, tuition reimbursement, and more.

Miami, Florida: $115,000 - $184,000 USD