MastercardDirector Data Engineering
AI in this role
Lead strategic data engineering initiatives to build large-scale data platforms supporting analytics, AI, and enterprise data ecosystems.
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
Director Data EngineeringDirector, Data EngineeringData Collection & Engineering (DC&E)
Overview
Mastercard powers a connected and inclusive digital economy by delivering secure, resilient, and intelligent payment technologies across the globe. Every day, Mastercard processes billions of payment interactions and generates massive volumes of transaction, customer, merchant, fraud, and operational data that power critical business decisions and innovative products.
As a Director, Data Engineering within the Data Collection & Engineering (DC&E) organisation, you will lead strategic data engineering initiatives that enable Mastercard's enterprise data ecosystem. You will be responsible for defining and executing the technology vision for large-scale Data Warehouse, Data Lakehouse, and Data Platform solutions supporting advanced analytics, AI, product innovation, regulatory reporting, and operational intelligence.
This role requires a combination of strong people leadership, executive stakeholder management, and deep technical expertise in modern data platforms, cloud-native architectures, distributed processing frameworks, and enterprise-scale data governance.
The ideal candidate will have a proven track record of building high-performing engineering organisations, driving large-scale cloud modernisation programmes, and delivering business outcomes through data at global scale.
Role
Strategic Leadership
• Define and execute the long-term Data Engineering strategy aligned with Mastercard's AI, analytics, digital payments, and data platform roadmap.
• Partner with business, product, architecture, security, and technology leaders to translate strategic objectives into scalable data solutions.
• Drive organisational transformation initiatives involving cloud migration, platform modernisation, automation, and engineering excellence.
• Develop multi-year roadmaps for data platform evolution, capacity growth, and technology investment planning.
Data Platform Architecture
• Lead the architecture and implementation of highly scalable, secure, and resilient Data Lakehouse and Data Warehouse platforms.
• Establish enterprise standards for data ingestion, transformation, storage, governance, lineage, metadata management, observability, and quality control.
• Drive adoption of reusable engineering frameworks and platform capabilities that enable self-service data products and accelerated delivery.
• Ensure architecture supports a "Build Once, Deploy Anywhere" model across on-premises, hybrid, and public cloud environments.
Cloud Modernisation & Engineering Excellence
• Lead large-scale migration programmes moving enterprise data workloads from legacy platforms to cloud-native architectures.
• Establish best practices for distributed processing, workflow orchestration, platform automation, and infrastructure optimisation.
• Drive adoption of modern DataOps, DevOps, CI/CD, and Infrastructure-as-Code practices across the engineering organisation.
• Establish operational standards for platform resiliency, disaster recovery, performance engineering, and capacity management.
Engineering Organisation Leadership
• Build, lead, and develop high-performing teams comprising Managers, Principal Engineers, Lead Engineers, and Senior Data Engineers.
• Foster a culture of innovation, accountability, technical excellence, collaboration, and continuous learning.
• Drive workforce planning, succession management, talent acquisition, skills development, and employee engagement initiatives.
• Provide coaching and mentorship to technology leaders and senior engineering talent.
Delivery & Stakeholder Management
• Oversee delivery of multiple strategic programmes supporting Mastercard's analytics, data science, product development, fraud, and regulatory functions.
• Manage complex stakeholder relationships across business units, executive leadership teams, and external partners.
• Ensure project execution aligns with business priorities, timeline commitments, financial objectives, and risk management standards.
• Establish governance frameworks and operating models that improve delivery predictability and engineering productivity.
Innovation & Emerging Technology
• Evaluate emerging technologies and define Mastercard's point of view on modern data engineering, AI-enabled data platforms, real-time analytics, and next-generation lakehouse architectures.
• Sponsor proof-of-concept initiatives and technology evaluations that drive competitive advantage and business value.
• Champion adoption of GenAI-enabled engineering practices, metadata-driven frameworks, and intelligent automation.
Operational Excellence
• Establish engineering KPIs, SLAs, and platform health metrics to continuously improve service reliability and operational efficiency.
• Lead resolution of high-severity production incidents and implement systemic improvements to prevent recurrence.
• Ensure compliance with Mastercard's security, privacy, regulatory, and engineering governance standards.
All About You
Leadership Experience
• 15+ years of experience delivering enterprise-scale Data Warehouse, Data Lake, Lakehouse, and Big Data solutions.
• 7+ years of leadership experience managing engineering managers, architects, and distributed engineering teams.
• Proven success leading global engineering organisations across multiple business domains and technology platforms.
• Experience managing large programmes involving multiple teams, stakeholders, vendors, and strategic initiatives.
Data Engineering Expertise
• Deep expertise designing and implementing large-scale data platforms using: Apache Kafka, Apache Spark, Scala/Java/Python, Hadoop ecosystem technologies and Distributed storage and compute architectures
• Extensive experience building: Enterprise Data Warehouses, Data Lakes, Lakehouse platforms, Real-time and batch processing frameworks, Enterprise metadata and governance platforms
• Strong understanding of: Data Modelling, Data Governance, Data Quality Management, Data Cataloguing, Data Lineage, Data Security and Master Data Management (MDM)
Cloud & Platform Engineering
• Extensive experience designing cloud-native architectures on AWS and hybrid cloud environments.
• Strong experience with: Amazon S3, EMR, Glue, Athena, EKS, Airflow, NiFi, Streaming and event-driven architectures
• Experience implementing:CI/CD pipelines, Infrastructure as Code, DataOps frameworks, Platform observability and monitoring solutions
Database & Analytical Technologies
• Strong expertise with Relational and NoSQL technologies: Oracle, SQL Server, Cassandra and other Distributed databases
• Advanced SQL optimisation and performance tuning skills.
Business & Executive Leadership
• Ability to connect technology investments with measurable business outcomes.
• Strong executive communication and presentation skills.
• Experience influencing Vice Presidents, Senior Vice Presidents, and executive stakeholders.
• Proven ability to balance innovation, delivery commitments, operational excellence, and financial accountability.
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.
How we rate this
Director Data Engineering at Mastercard rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a workflow you automated with AI tools, end to end.
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where cloud architecture was part of your work. What did you do?
- Tell me about a project where data governance was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Automation, Data Engineering, Cloud Architecture, Data Governance, and Distributed Systems. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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