Lead Data Engineer

Our Purpose

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. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Lead Data Engineer

Overview:

Mastercard’s Enterprise Credit Risk (ECR) team is seeking a Lead Data Engineer to design and scale the next generation of data platforms that power credit decisioning, portfolio risk management, regulatory reporting, analytics, and AI-driven insights across Mastercard’s lending and risk ecosystems.

In this role, you will combine deep hands-on data engineering expertise with technical leadership to build reliable, scalable, governed, and high-quality data products. You will lead the architecture and implementation of modern data engineering solutions across cloud platforms, large-scale data processing, data governance, and operational excellence.

You will partner closely with Product Management, Risk Analytics, Data Science, Architecture, Engineering, and Business stakeholders to transform complex business needs into scalable technical solutions, simplify access to trusted data, and accelerate innovation across the ECR portfolio.

This is a hybrid role based in O’Fallon, MO, requiring three days per week onsite.

Role:

• Lead the design, development, and evolution of enterprise-grade data platforms and pipelines supporting credit risk products, decisioning capabilities, analytics, and regulatory reporting.
• Architect and implement scalable ETL/ELT frameworks using Databricks, Apache Spark, Delta Lake, and cloud-native technologies to support high-volume data workloads.
• Drive data platform modernization and cloud transformation, including the migration of legacy data assets to modern cloud-based and Data Lakehouse architectures.
• Establish and advance data quality, lineage, metadata, governance, observability, and monitoring capabilities to deliver trusted, compliant, and reliable data products.
• Partner across Risk, Product, Data Science, Architecture, Engineering, and Business teams to translate complex requirements into scalable, sustainable technical solutions.
• Define and promote engineering standards and best practices across coding, testing, data quality, deployment automation, documentation, and operational excellence.
• Lead technical design reviews and influence architectural decisions for data-intensive applications, platforms, and services.
• Optimize large-scale data workloads and platforms for performance, reliability, scalability, resiliency, and cost efficiency.
• Build reusable, governed data products and capabilities that enable AI, machine learning, advanced analytics, and emerging risk use cases.
• Drive technical initiatives from strategy through execution, identifying dependencies, managing technical risks, and ensuring solutions align with business priorities and long-term platform strategy.
• Mentor and coach engineers across the organization, providing technical guidance, design expertise, knowledge sharing, and feedback to strengthen engineering capabilities.
• Shape technology roadmaps and delivery priorities across the ECR portfolio, balancing business value, engineering feasibility, risk, compliance, and long-term platform sustainability.
• Ensure engineering solutions meet security, privacy, regulatory, compliance, and audit requirements through appropriate controls, documentation, and engineering practices.
• Create clarity in complex and ambiguous environments, communicate technical trade-offs to senior stakeholders, and foster a culture of ownership, accountability, and continuous improvement.

All About You:

• Deep expertise in designing and implementing large-scale data engineering solutions and distributed data processing systems.
• Advanced proficiency with Databricks, Apache Spark/PySpark, Delta Lake, SQL, and Python.
• Experience building and operating cloud-based data platforms using Azure, AWS, or GCP.
• Strong experience developing enterprise-grade ETL/ELT pipelines, data integration frameworks, and streaming architectures.
• Strong understanding of data modeling and data architecture for analytical and operational workloads.
• Experience implementing data quality, lineage, metadata management, observability, and data governance frameworks in enterprise environments.
• Experience working with modern data formats and storage technologies, including Parquet, Avro, and ORC.
• Working knowledge of CI/CD, infrastructure-as-code, automated testing, Git/GitHub, and DevOps practices for data engineering environments.
• Experience with workflow orchestration tools, such as Apache Airflow.
• 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.
• Experience with Java-based application development is a strong plus, particularly in environments where data platforms integrate with enterprise applications and services.

Preferred Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline. Equivalent practical experience will also be considered.
• Preferred experience with:

o Databricks

o Apache Spark / PySpark

o Hadoop

o Delta Lake

o SQL and Python

o Apache Airflow

o Azure Data Services

o Kafka / event streaming

o GitHub and CI/CD tooling

o Cloud-based Data Lakehouse architectures

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. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• 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, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

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