MastercardPrincipal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow)
Mastercard is hiring a Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow) for a remote role open to applicants in India. Level rates it ; you can apply on Mastercard.
Level's read on this job
- On Level's scale, this data engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting lists this role as remote.
- Tools and skills the posting cites include MLflow, Databricks and AWS.
- Level classifies it as a lead role.
- Level lists 967 other open roles at Mastercard.
AI in this role
Design and build robust data platforms and pipelines to support advanced analytics, machine learning, and AI workloads.
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
Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow)OverviewMastercard Foundry R&D develops emerging technology solutions and transforms promising concepts into scalable products and platforms.
We are seeking a Principal Data Engineer to design and build the data foundations that power advanced analytics, machine learning, generative AI, and agentic AI solutions. This role requires a hands-on technical leader who can turn ambiguous R&D objectives into secure, scalable, and production-ready data systems.
You will collaborate with AI Engineers, Data Scientists, Researchers, Software Engineers, and Product Managers to create reusable data capabilities that accelerate experimentation and the delivery of innovative products.
Role
What You'll Do
• Lead the architecture, design, and engineering of scalable batch, streaming, and event-driven data platforms.
• Design and build reliable data ingestion, transformation, enrichment, processing, and consumption pipelines.
• Develop governed lakehouse and modern data platform architectures using cloud-native technologies and distributed data processing frameworks.
• Build and operate platforms that support analytics, machine learning, generative AI, and agentic AI workloads.
• Create reusable data products, services, APIs, and self-service capabilities that accelerate experimentation and production delivery.
• Enable the full AI and machine learning lifecycle through robust feature engineering, training, evaluation, deployment, and inference pipelines.
• Optimize data workloads for performance, scalability, reliability, resiliency, and cost efficiency.
• Define engineering standards for data modeling, software development, testing, CI/CD, observability, and operational excellence.
• Implement data governance capabilities including quality controls, lineage, metadata management, cataloging, access control, retention, and compliance.
• Apply security and privacy-by-design principles for sensitive and regulated data environments.
• Evaluate emerging data and AI technologies through prototypes, proof-of-concepts, and technical assessments.
• Translate loosely defined research, innovation, or product requirements into practical architectures and incremental delivery plans.
• Provide hands-on technical leadership through architecture reviews, code reviews, troubleshooting, and engineering mentorship.
• Partner with cross-functional teams to mature successful R&D initiatives into enterprise-grade production solutions.
• Communicate technical decisions, trade-offs, risks, dependencies, and roadmap recommendations to both technical and business stakeholders.
• Drive adoption of modern data engineering practices, platform automation, and platform reliability disciplines.
All About You
Required Qualifications
• Typically 12-18 years of overall career relevant experience, including significant ownership of complex enterprise data engineering solutions.
• Extensive experience designing, building, and operating large-scale production data platforms.
• Expert programming skills in Python, PySpark, and modern software engineering practices.
• Advanced SQL expertise, including data modeling, performance tuning, query optimization, and large-scale analytical processing.
• Strong hands-on experience with distributed data processing technologies such as Apache Spark and modern lakehouse platforms.
• Experience building and operating cloud-native data platforms in AWS, or Azure, or other enterprise cloud environments.
• Strong knowledge of modern data architecture patterns, including data lakes, lakehouses, data meshes, data products, and event-driven architectures.
• Experience with cloud storage, data integration, streaming, serverless, observability, and security services across public cloud platforms.
• Experience building scalable batch and real-time data pipelines.
• Experience with workflow orchestration platforms such as Apache Airflow, Databricks Workflows, AWS Step Functions, Azure Data Factory, or similar technologies.
• Experience implementing CI/CD, automated testing, version control, Infrastructure as Code, and platform automation.
• Strong understanding of data governance, metadata management, lineage, quality frameworks, privacy controls, and access management.
• Experience diagnosing and resolving complex performance, reliability, scalability, and operational challenges.
• Ability to make sound architectural decisions while balancing delivery speed, innovation, maintainability, security, and cost.
• Proven ability to thrive in R&D and innovation-focused environments where priorities and requirements may evolve through experimentation.
• Strong communication, collaboration, technical leadership, and mentoring skills.
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical discipline, or equivalent practical experience.
Preferred Qualifications
• Experience supporting machine learning, generative AI, or agentic AI systems.
• Experience with MLflow or comparable ML lifecycle tooling.
• Experience with Azure Machine Learning, Azure AI Foundry, or Microsoft Fabric.
• Experience with Unity Catalog, Microsoft Purview, or comparable catalog and governance platforms.
• Experience with Kafka or other event-streaming technologies.
• Experience with containerized and cloud-native platforms, including Docker and Kubernetes.
• Experience with Terraform or another Infrastructure as Code framework.
• Experience integrating structured, semi-structured, unstructured, and streaming data.
• Knowledge of responsible AI, model evaluation, and AI platform observability.
• Experience in payments, financial services, or another regulated industry.
Success in This Role
Success requires someone who:
• Remains strongly hands-on while providing technical direction.
• Can build production-quality systems without introducing unnecessary platform complexity.
• Converts experimentation into reusable engineering capabilities.
• Designs for security, governance, reliability, and observability from the outset.
• Challenges assumptions and validates architectural choices through evidence and prototypes.
• Enables AI and product teams rather than taking ownership of data science or model research.
• Influences across teams without depending on formal people-management authority.
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
Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow) at Mastercard rates 70 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. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data engineer job
- $145k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where data architecture was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Evaluation, AI Safety, Data Engineering, Data Architecture, and Machine learning. 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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