PUNE · FULLTIME
Principal Data Engineer
Mastercard
Pune · onsite · Posted 64d ago
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Undisclosed7–11 yrsfulltimePune
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Section · 01
About this role
JOB DESCRIPTION Our Purpose
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
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
Principal Data Engineer | Data Platform Architecture | Distributed Data Processing
Role Overview We are seeking a highly experienced Principal Data Engineer to lead the architecture, engineering, and modernization of Mastercard's Rewards Data Platform, supporting 400+ customers globally and processing millions of transactions daily.
This role serves as a senior technical leader responsible for defining data engineering strategy, architecture standards, workflow orchestration frameworks, and scalable data processing solutions that support critical business operations. The ideal candidate will possess deep expertise in distributed data processing, data integration, data modeling, and data platform architecture, while influencing engineering best practices across teams and programs.
The successful candidate will operate as a recognized subject matter expert, providing hands-on technical leadership, mentorship, and guidance in complex, large-scale, and evolving environments.
Key Responsibilities: Data Platform Architecture Modernization - Define and drive the strategic architecture for enterprise-scale data platforms and data ecosystems.
- Lead modernization initiatives from legacy platforms to scalable, cloud-native data architectures.
- Establish standards and best practices for data modeling, data integration, data storage, governance, security, and operational excellence.
- Design scalable and resilient solutions supporting transactional, analytical, and operational workloads.
- Identify technical debt and develop modernization roadmaps aligned with business objectives.
- Influence architecture decisions across engineering teams, partner platforms, and enterprise initiatives.
- Architect and guide high-volume batch and real-time data processing solutions.
- Design and optimize distributed data processing frameworks for large-scale workloads.
- Lead implementation of ETL/ELT pipelines, workflow orchestration, data ingestion, transformation, reconciliation, and integration capabilities.
- Establish standards for data quality, lineage, monitoring, validation, auditing, and error handling.
- Drive performance optimization, scalability, reliability, and operational efficiency across data platforms.
- Support solutions involving Data Lakes, Lakehouse architectures, relational databases, NoSQL platforms, streaming technologies, and cloud data services.
- Define enterprise patterns for workflow orchestration and workflow management across critical business processes.
- Design automated, scalable, and resilient workflows that coordinate data movement, processing, and downstream consumption.
- Enable seamless integration across internal systems, partner platforms, and enterprise services.
- Improve operational efficiency through automation, monitoring, and workflow optimization.
- Ensure reliable execution, dependency management, fault tolerance, and recovery mechanisms across data workflows.
- Ensure data platforms adhere to enterprise security, privacy, compliance, and governance standards.
- Define and implement controls for data protection, access management, auditability, and regulatory compliance.
- Promote best practices for secure data design, storage, processing, and transmission.
- Partner with security and compliance teams to maintain enterprise-grade data controls.
- Provide hands-on leadership for complex data engineering and platform initiatives.
- Conduct architecture and design reviews to ensure consistency, scalability, and engineering quality.
- Develop reusable frameworks, standards, and engineering accelerators.
- Mentor engineers and technical leaders on data engineering, workflow orchestration, distributed systems, and platform best practices.
- Shape engineering standards and influence strategic technology decisions across multiple teams and programs.
- Proven experience as a Principal Data Engineer, Lead Data Engineer, or Data Architect in enterprise-scale environments.
- Deep expertise in:
- Workflow Orchestration
- Distributed Data Processing
- Data Engineering
- Data Integration
- Data Lakes and Lakehouse Architectures
- Data Modeling
- Database Design
- Data Security
- Workflow Management
- Strong experience with technologies such as:
- Apache Spark
- Databricks
- Hadoop Ecosystem
- Kafka or Streaming Platforms
- Relational and NoSQL Databases
- Cloud Data Platforms
- Experience designing and operating high-volume, mission-critical, multi-tenant data platforms.
- Strong knowledge of ETL/ELT frameworks, data pipelines, workflow automation, and distributed data architectures.
- Experience with Azure, AWS, or GCP cloud environments.
- Understanding of Kubernetes/OpenShift, CI/CD, observability, and platform automation.
- Strong focus on performance, scalability, resiliency, operational excellence, and secure-by-design principles.
- Recognized subject matter expert with the ability to lead in complex and evolving technical environments.
- Experience defining engineering standards, best practices, and architectural frameworks.
- Proven ability to influence decisions across organizations and multiple engineering teams.
- Strong mentoring and coaching experience for engineers and technical leaders.
- Excellent stakeholder management and communication skills.
- Experience supporting large-scale financial services, payments, loyalty, or rewards platforms.
- Experience with enterprise Data Lake, Lakehouse, and distributed processing platforms.
- Experience leading cloud migration and data platform modernization initiatives.
- Familiarity with analytics platforms and AI/ML data enablement.
- Experience working in highly regulated environments with strong security and compliance requirements.
Acts as a recognized subject matter expert, applies expertise in ambiguous and complex environments, shapes organizational best practices, drives strategic technical direction, and regularly mentors and develops other engineers.
Corporate Security Responsibility - 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.
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Locations Location: Pune, India, 411006
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Section · 02
Skills
Apache SparkApache KafkaDatabase Designdata architectureworkflow managementmentoringApache HadoopData LakeDatabricksData EngineeringData SecurityCommunication SkillsArchitectureData IntegrationData ProcessingETLData ModelingData PipelineCoaching
Section · Company
About MasterCard
Mastercard
FinTech
7.9k+
employees
1966
60 years old
Purchase,New York
United States
About
Payments Technology company enabling transactions
Industries
FinTechFinancial ServicesInternet
Employee ratings
1,001 reviews
3.8/ 5
Culture
3.8
Career growth
3.1
Work-life
3.8
Employees rate it well for
Work Life Balance231Work Culture142Salary110Senior Management75Job Security55
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