Vice President, Data Engineering

Mastercard Mastercard · Fintech · San Francisco, CA +1 · AI & Data

Mastercard is seeking a Vice President of Data Engineering to lead the transformation of their enterprise data ecosystem and unlock the full value of their data assets. This role will drive innovation in managing, storing, governing, and accessing large-scale data across public cloud and on-premise environments, establishing consistent engineering standards and principles. The position involves defining and delivering modern, scalable, and secure data and AI platforms on AWS and Azure, focusing on platform strategy, enterprise governance, engineering excellence, and best practices. It's a hands-on technical leadership role with deep involvement in data engineering and architecture design and implementation, with the potential to build and lead a team.

What you'd actually do

  1. Define and lead the architecture of end-to-end cloud-native data platforms, including lakehouse architectures built on S3, Azure Data Lake, Databricks, and related technologies.
  2. Provide technical leadership across data engineering, platform engineering, and DevOps organizations, guiding engineering leaders and cross-functional teams
  3. Act as a hands-on architect and engineer, contributing directly to the design, development, and implementation of core data platform components.
  4. Lead DevOps transformation initiatives, including CI/CD pipelines, infrastructure-as-code, and fully automated multi-cloud deployment models.
  5. Establish and enforce enterprise cloud security standards, including IAM, encryption, network security, and regulatory compliance (e.g., GDPR, HIPAA)

Skills

Required

  • Proven experience as a Director or Vice President of Data Engineering, Data Architecture, or similar senior technical leadership roles.
  • Strong hands-on experience in data engineering and architecture, with the ability to design and implement solutions directly when needed.
  • Experience leading or scaling globally distributed engineering teams across multiple geographies.
  • Deep expertise in AWS and Azure services, including S3, EC2, Lambda, Glue, Flink, Lake Formation, Azure Data Factory, Azure Fabric, Synapse, and Databricks.
  • Strong experience building DevOps pipelines and infrastructure-as-code using Terraform, CloudFormation, ARM templates, Azure DevOps, or GitLab CI/CD.
  • Strong understanding of cloud security architecture, including IAM design, encryption strategies, and governance frameworks.
  • Experience with containerization and orchestration technologies such as Docker, Kubernetes, ECS, or AKS.
  • Strong background in data engineering frameworks such as Spark, PySpark, and large-scale distributed ETL systems.
  • Proven success delivering production-grade Lakehouse architectures using the medallion model.
  • Strong leadership skills with demonstrated ability to influence senior stakeholders and drive cross-functional alignment.
  • Experience with data governance platforms such as AWS Lake Formation or Azure Purview.
  • Familiarity with observability and monitoring tools (CloudWatch, Azure Monitor, ELK, etc.)
  • Experience with cloud cost optimization and financial accountability for large-scale platforms.
  • Exposure to MLOps practices and integration of machine learning pipelines into data platforms.
  • Strong experience working in Agile/Scrum environments.
  • Strong executive communication and stakeholder management skills, with demonstrated experience engaging and influencing senior leadership and executive audiences, and the ability to convey complex ideas clearly to diverse audiences.
  • Bachelor’s degree in a quantitative field such as Engineering, Mathematics, Computer Science, or related discipline, or equivalent practical experience.

Nice to have

  • MLOps practices and integration of machine learning pipelines into data platforms

What the JD emphasized

  • enterprise cloud security standards
  • regulatory compliance (e.g., GDPR, HIPAA)