Manager - Data Engineering

Mastercard is hiring a Manager - Data Engineering in Pune.

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 Manager - Data Engineering

Responsibilities

Data Platform Engineering

• 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.

Platform & Infrastructure Integration

• 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.

AI Enablement

• Partner with AI/ML teams to ensure the data platform effectively supports AI-driven use cases (e.g., enrichment, search, anomaly detection).

Requirements

Experience

• Strong experience in data engineering, platform engineering, or distributed systems.
• Experience leading enterprise-scale data engineering teams.
• Proven track record owning and operating mission-critical data platforms in production environments.

Core Technical Skills

• Strong architectural understanding of enterprise data platforms and distributed data systems.
• Hands-on background (current or prior) with:

o Apache Spark (batch; streaming preferred) o Apache NiFi or comparable ingestion frameworks o 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.

Preferred qualifications

• Experience with Kafka or event-driven architectures.
• Familiarity with lakehouse technologies (Parquet, Delta Lake, Iceberg, or Hudi).
• Experience enabling AI/ML use cases through data platforms (not model development).
• Exposure to monitoring and observability stacks (e.g., Prometheus, Grafana, ELK).
• Background in regulated or security-conscious environments (e.g., financial services).

Soft Skills

• 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.

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.

About Mastercard

We are looking for a Manager, 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 Manager will ensure the platform ena