Lead Service Management Engineer

We are seeking a Lead Data Engineer to join Mastercard Architecture & Analytics team. You will help shape our innovation roadmap by exploring new technologies and building scalable, data-driven prototypes and products. The ideal candidate is hands-on, curious, adaptable, and motivated to experiment and learn.What You'll Do* Drive Data Architecture: Own the data architecture and modeling strategy for AI projects.

Define how data is stored, organized, and accessed. Select technologies, design schemas/formats, and ensure systems support scalable AI and analytics workloads.* Build Scalable Data Pipelines: Lead development of robust ETL/ELT workflows and data models. Build pipelines that move large datasets with high reliability and low latency to support training and inference for AI and generative AI systems.* Ensure Data Quality & Governance: Oversee data governance and compliance with internal standards and regulations.

Implement data anonymization, quality checks, lineage, and controls for handling sensitive information.* Provide Technical Leadership: Offer hands-on leadership across data engineering projects. Conduct code reviews, enforce best practices, and promote clean, well-tested code. Introduce improvements in development processes and tooling.* Cross-Functional Collaboration: Work closely with engineers, scientists, and product stakeholders.

Scope work, manage data deliverables in agile sprints, and ensure timely delivery of data components aligned with project milestones.What You'll Bring* Extensive Data Engineering

All About you - 8-12+ years in data engineering or backend engineering, including senior/lead roles. Experience designing end-to-end data systems, solving scale/performance challenges, integrating diverse sources, and operating pipelines in production.* Big Data & Cloud Expertise: Strong skills in Python and/or Java/Scala.

Deep experience with Spark, Hadoop, Hive/Impala, and Airflow. Hands-on work with AWS, Azure, or GCP using cloud-native processing and storage services (e.g., S3, Glue, EMR, Data Factory). Ability to design scalable, cost-efficient workloads for experimental and variable R&D environments.* AI/ML Data Lifecycle Knowledge: Understanding of data needs for machine learning-dataset preparation, feature/label management, and supporting real-time or batch training pipelines.

Experience with feature stores or streaming data is useful.* Leadership & Mentorship: Ability to translate ambiguous goals into clear plans, guide engineers, and lead technical execution.* Problem-Solving Mindset: Approach issues systematically, using analysis and data to select scalable, maintainable solutions.Required Skills*