Senior Data Engineer
Mastercard•Miami, FL
•$115,000 - $184,000•Onsite
About The Position
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. The ideal candidate is a hands-on engineer who thrives in modern Lakehouse architecture, large-scale pipeline development and analytical mindset.
Requirements
- 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 DevOps practices.
- Experience with data governance, data quality frameworks, and metadata management tools.
- Strong experience with AWS Cloud services, including services such as Amazon S3, EMR, Glue, Lambda, ECS/EKS, and CloudWatch for developing, deploying, and managing cloud-native data solutions.
Responsibilities
- 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, CloudWatch
- Implement CI/CD pipelines, automated testing, and Infrastructure-as-Code
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
Education Level
No Education Listed
