Lead Data Engineer

Role: • Design and develop cloud-native data and BI solutions using Databricks and Snowflake on AWS. • Build scalable, high-performance data pipelines and analytics frameworks supporting enterprise data products. • Drive implementation of multi-cloud architectures across AWS and Azure, including Azure Databricks and ADLS Gen2. • Enable seamless integration and interoperability across Databricks, Snowflake, AWS services, and Azure services. • Build and maintain CI/CD pipelines, automation frameworks, and Infrastructure as Code for reliable data platform provisioning and deployment. • Implement secure and governed access patterns using RBAC, IAM, service principals, and enterprise identity integration. • Design reusable platform capabilities that enable data interoperability across multiple compute engines and cloud providers. • Establish engineering standards for development, testing, deployment, monitoring, documentation, and operational excellence. • Optimize data platforms and workloads for performance, scalability, reliability, resiliency, and cost efficiency. • Lead technical design reviews and influence architecture and platform engineering decisions. • Partner with Product, Architecture, Engineering, Data, and Business teams to translate complex requirements into scalable and sustainable technical solutions. • Provide technical leadership and mentorship to engineers while driving initiatives from design through implementation. All About You: • Deep hands-on experience as a Lead or Senior Data Engineer, designing and building enterprise-scale cloud data, analytics, and BI platforms. • Strong expertise with Databricks, Snowflake, and AWS, including AWS services such as S3, Glue, EMR, and IAM. • Advanced proficiency with Apache Spark/PySpark, Delta Lake, SQL, and Python for large-scale data processing and analytics. • Experience building scalable ETL/ELT pipelines, data integration frameworks, and distributed data solutions. • Experience designing multi-cloud data architectures and integrations across AWS and Azure; experience with Azure Databricks and ADLS Gen2 is a plus. • Strong experience with CI/CD, Infrastructure as Code, automation, Git/GitHub, and DevOps practices for data platforms. • Experience implementing enterprise security, governance, and access controls, including RBAC, IAM, service principals, data lineage, metadata management, and observability. • Knowledge of modern and open data architectures and formats, including Delta Lake and Apache Iceberg. • Understanding of FinOps, cloud cost optimization, performance, and scalability for large-scale data workloads. • Proven ability to lead technical design and architecture decisions, drive complex engineering initiatives across teams, and mentor engineers. • Strong communication and stakeholder management skills, with the ability to clearly articulate technical decisions, trade-offs, risks, and recommendations. • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline. Equivalent practical experience will also be considered. 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.

Lead Data Engineer Overview: Mastercard’s Data Commercialization Platform (DCP) team is seeking a Lead Data Engineer to design, build, and scale cloud-native data and Business Intelligence solutions across a modern multi-cloud data ecosystem. This role will focus heavily on Databricks, Snowflake, and AWS, building scalable data pipelines, analytics frameworks, and platform capabilities that support enterprise data products. You will also help drive a multi-cloud architecture across AWS and Azure, enabling secure and governed interoperability across platforms, compute engines, and cloud providers. The ideal candidate combines strong hands-on engineering expertise with technical leadership and can translate complex platform requirements into scalable, reliable, secure, and cost-effective solutions. This is a hybrid role based in Arlington, VA requiring three days per week onsite. Role: • Design and develop cloud-native data and BI solutions using Databricks and Snowflake on AWS. • Build scalable, high-performance data pipelines and analytics frameworks supporting enterprise data products. • Drive implementation of multi-cloud architectures across AWS and Azure, including Azure Databricks and ADLS Gen2. • Enable seamless integration and interoperability across Databricks, Snowflake, AWS services, and Azure services. • Build and maintain CI/CD pipelines, automation frameworks, and Infrastructure as Code for reliable data platform provisioning and deployment. • Implement secure and governed access patterns using RBAC, IAM, service principals, and enterprise identity integration. • Design reusable platform capabilities that enable data interoperability across multiple compute engines and cloud providers. • Establish engineering standards for development, testing, deployment, monitoring, documentation, and operational excellence. • Optimize data platforms and workloads for performance, scalability, reliability, resiliency, and cost efficiency. • Lead technical design reviews and influence architecture and platform engineering decisions. • Partner with Product, Architecture, Engineering, Data, and Business teams to translate complex requirements into scalable and sustainable technical solutions. • Provide technical leadership and mentorship to engineers while driving initiatives from design through implementation.

Overview: Mastercard’s Data Commercialization Platform (DCP) team is seeking a Lead Data Engineer to design, build, and scale cloud-native data and Business Intelligence solutions across a modern multi-cloud data ecosystem. This role will focus heavily on Databricks, Snowflake, and AWS, building scalable data pipelines, analytics frameworks, and platform capabilities that support enterprise data products. You will also help drive a multi-cloud architecture across AWS and Azure, enabling secure and governed interoperability across platforms, compute engines, and cloud providers. The ideal candidate combines strong hands-on engineering expertise with technical leadership and can translate complex platform requirements into scalable, reliable, secure, and cost-effective solutions. This is a hybrid role based in Arlington, VA requiring three days per week onsite. Role: • Design and develop cloud-native data and BI solutions using Databricks and Snowflake on AWS. • Build scalable, high-performance data pipelines and analytics frameworks supporting enterprise data products. • Drive implementation of multi-cloud architectures across AWS and Azure, including Azure Databricks and ADLS Gen2. • Enable seamless integration and interoperability across Databricks, Snowflake, AWS services, and Azure services. • Build and maintain CI/CD pipelines, automation frameworks, and Infrastructure as Code for reliable data platform provisioning and deployment. • Implement secure and governed access patterns using RBAC, IAM, service principals, and enterprise identity integration. • Design reusable platform capabilities that enable data interoperability across multiple compute engines and cloud providers. • Establish engineering standards for development, testing, deployment, monitoring, documentation, and operational excellence. • Optimize data platforms and workloads for performance, scalability, reliability, resiliency, and cost efficiency. • Lead technical design reviews and influence architecture and platform engineering decisions. • Partner with Product, Architecture, Engineering, Data, and Business teams to translate complex requirements into scalable and sustainable technical solutions. • Provide technical leadership and mentorship to engineers while driving initiatives from design through implementation. All About You: • Deep hands-on experience as a Lead or Senior Data Engineer, designing and building enterprise-scale cloud data, analytics, and BI platforms. • Strong expertise with Databricks, Snowflake, and AWS, including AWS services such as S3, Glue, EMR, and IAM. • Advanced proficiency with Apache Spark/PySpark, Delta Lake, SQL, and Python for large-scale data processing and analytics. • Experience building scalable ETL/ELT pipelines, data integration frameworks, and distributed data solutions. • Experience designing multi-cloud data architectures and integrations across AWS and Azure; experience with Azure Databricks and ADLS Gen2 is a plus. • Strong experience with CI/CD, Infrastructure as Code, automation, Git/GitHub, and DevOps practices for data platforms. • Experience implementing enterprise security, governance, and access controls, including RBAC, IAM, service principals, data lineage, metadata management, and observability. • Knowledge of modern and open data architectures and formats, including Delta Lake and Apache Iceberg. • Understanding of FinOps, cloud cost optimization, performance, and scalability for large-scale data workloads. • Proven ability to lead technical design and architecture decisions, drive complex engineering initiatives across teams, and mentor engineers. • Strong communication and stakeholder management skills, with the ability to clearly articulate technical decisions, trade-offs, risks, and recommendations. • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline. Equivalent practical experience will also be considered.