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Mastercard is seeking a Lead Data Engineer to drive the design, development, and optimization of scalable data solutions that power analytics, experimentation, and decision-making across the organization. In this role, you will serve as both a technical expert and a team leader—overseeing data pipeline architecture, ensuring data quality and reliability, and guiding engineers in best practices. You will collaborate closely with cross-functional partners to enable high-impact data products while shaping the long-term vision for data engineering capabilities.
Job Responsibility:
Lead the design, development, and maintenance of scalable, reliable data pipelines and data processing frameworks supporting a variety of business and product use cases
Ensure data quality, integrity, and readiness by establishing and maintaining standards, validation processes, and monitoring frameworks
Mentor and guide a team of data engineers, fostering strong technical craftsmanship, collaboration, and continuous learning
Collaborate with cross-functional teams (Data Science, Product, Analytics, Infrastructure, and Engineering) to deliver end-to-end data solutions
Drive technical roadmap and architectural decisions, ensuring scalability, performance, and long-term sustainability of data systems
Identify and implement improvements to ETL/ELT processes, focusing on automation, efficiency, and operational excellence
Evaluate and integrate emerging technologies to enhance data engineering capabilities and support evolving business needs
Oversee complex data projects, ensuring timely, high-quality delivery while balancing multiple priorities
Act as a subject matter expert on data modeling, pipeline optimization, large-scale data processing, and best practices
Ensure compliance with internal policies and external data regulations, promoting secure and responsible data usage across the team
Requirements:
Extensive experience as a Data Engineer or in a similar role, with deep expertise in data engineering principles, data modeling, and pipeline development
Strong hands-on skills in SQL and experience with major RDBMS or cloud data warehouses
Strong hands on skills with AWS Data Engineering tech stack (EMR, Glue, Kinesis, Lambda etc.)
Good hands on skill with orchestration tools like Apache Airflow
Hands on experience with AWS AI and ML services like Sage maker, Bedrock
Experience working with Databricks, delta and ICEBERG open table formats
Proficiency with at least one programming or scripting language such as Python, Scala, or PowerShell
Experience working with big data and distributed systems (e.g., Spark, Hadoop, cloud-native big data services)
Strong understanding of data quality frameworks, validation methods, and monitoring tools
Familiarity with Agile methodologies and modern DevOps practices for data engineering
Proven ability to lead technical teams, manage multiple projects, and work effectively across geographies and functions
Ability to translate complex business problems into scalable technical solutions
Excellent communication skills, with the ability to articulate data concepts to both technical and non-technical audiences
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience)