Director of Data Engineering, Mastercard

• Proven hands-on experience in data engineering, platform engineering, or distributed systems
• Previous experience leading enterprise-scale data engineering teams
• Proven track record owning and operating mission-critical data platforms in production environments
• Strong architectural understanding of enterprise data platforms and distributed data systems
• Hands-on background (current or prior) with:
•  Apache Spark (batch; streaming preferred)
•  Apache NiFi or comparable ingestion frameworks
•  Apache Airflow or similar orchestration tools
• Experience with object storage systems (S3-compatible storage)
• Experience operating data workloads on Kubernetes-based platforms
• Strong understanding of data modeling, schema evolution, pipeline design, and reliability patterns
• Exposure to public cloud platforms (AWS, Azure, or GCP) and hybrid deployment models
• Ability to apply cloud-native design principles to guide platform modernization and migration strategies
• Strong ownership mindset and accountability for enterprise platforms
• Excellent executive communication and stakeholder management skills
• Pragmatic decision-making in complex, ambiguous environments
• Proven ability to build, mentor, and retain high-performing engineering leaders
• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field. Equivalent practical experience may also be considered

Desirable

• We are looking for a Director, Data Engineering to lead the strategy, design, and operation of our enterprise-scale data platform that powers analytics, applications, and AI-enabled use cases across the organization
• This role is firmly grounded in data engineering and platform engineering—owning platform vision, architecture, and execution across ingestion, processing, orchestration, storage, reliability, and scalability for batch and streaming workloads
• The Director will ensure the platform enables advanced capabilities, including AI, while meeting enterprise standards for security, governance, and operational excellence
• The focus is on building and evolving robust data infrastructure that makes data and AI capabilities easy, safe, and scalable for downstream teams to consume, while positioning the platform for cloud modernization and long-term growth
• Strong technical credibility is expected, and the leader needs to be comfortable with hands on technology work
•  Own the end-to-end vision, roadmap, and architecture for the enterprise data platform
•  Provide technical and organizational leadership over scalable data pipelines using technologies such as Apache NiFi, Airflow, Spark (batch / streaming), and synonymous technologies across On-Prem and Cloud platforms
•  Ensure consistent design and governance of data ingestion, transformation, enrichment, and access patterns across teams
•  Define and govern data schemas, contracts, and transformations, ensuring data quality, consistency, and backward compatibility
•  Drive platform performance, scalability, reliability, and cost optimization across environments
•  Establish platform-wide data quality standards, monitoring, alerting, and SLAs for critical data assets
•  Oversee use of object storage platforms (MinIO / Ceph / S3-compatible APIs) including data layout, lifecycle management, and retention policies
•  Own operational readiness for batch and near–real-time processing, including incident management and root cause analysis
•  Provide architectural oversight for containerized data workloads and services deployed on Kubernetes-based platforms
•  Partner closely with DevOps, SRE, and Infrastructure teams to ensure observability, resiliency, and operational maturity
•  Guide CI/CD, automation, and infrastructure-as-code practices for data platform components
•  Lead platform modernization efforts, capacity planning, and preparation for hybrid or public cloud adoption
•  Partner with AI/ML teams to ensure the data platform effectively supports AI-driven use cases (e.g., enrichment, search, anomaly detection)
•  Define patterns for integrating AI-enabled capabilities (e.g., PII detection, classification, summarization) into enterprise data workflows
•  Ensure AI-enabled data pipelines comply with enterprise security, privacy, and governance requirements
•  Enable scalable, repeatable data foundations that allow AI teams to operate efficiently without direct platform customization
•  Act as a senior partner to application teams, analytics teams, AI teams, and product leaders to translate business needs into platform capabilities
•  Communicate platform strategy, risks, and trade-offs clearly to executive and senior stakeholder audiences
•  Establish documentation, standards, and best practices to support self-service and platform adoption
•  Build, mentor, and lead senior engineering managers and technical leaders, raising the overall engineering bar