Responsibilities

Data Analysis

• Conduct thorough data profiling and analysis to identify anomalies, inconsistencies, and inaccuracies in datasets

• Identify new methods of detecting data anomalies.

Automation

• Automate processes, data quality checks, and workflows to ease data validation processes across complex, interdependent data systems

Quality Engineering

• Implement, and maintain a robust data quality framework to assess, monitor, and report the quality of data across various systems and platforms

• Design and develop generic quality check frameworks that can be utilized across multiple products

• Develop and execute comprehensive data quality testing strategies and plans to verify the implementation of data pipelines and data validations.

• Develop and implement manual and automated test cases to ensure reliability of data pipelines, data migration processes and data transformations

• Design and implement intuitive metrics that show stake holders the health of their data in an actionable format

• Develop test strategies and validation steps for Analytical Data Models

• Conduct initial root cause analysis for data issues, collaborate with partners to clearly identify the issue, scope and impact, and path for research/solutioning

Documentation

• Create and maintain documentation related to data quality processes and standards

Reporting

• Establish monitoring mechanisms to proactively identify data quality issues, and generate regular reports on data quality metrics for review

Mentoring

• Provide training and guidance to team members on data quality best practices and principles. Facilitate knowledge sharing sessions to promote a culture of data quality awareness.

Collaboration

• Collect data quality requirements from key partners, seeking to understand the subjective “quality” measures that are important to data consumers to build and maintain trust in our data & products

• Collaborate with Data Engineers, Data Analysts, and business leaders to understand data quality challenges within data workflows and how the data is used by Mastercard products and customers

• Collaborate across teams as a data quality advocate, guiding on the need to balance which data/sources require high accuracy versus directionally accurate data

Ideal Candidate Qualifications:

• 8+ years of experience in the DataQuality/DataModeling/DataEngineering fields

• Extensive Python or R experience to develop and maintain data quality scripts, tools, and frameworks.

• Expert-level knowledge of SQL for complex data querying and manipulation

• Experience with analytical and predictive models

• Experience with various ETL transformations and workflows

• Experience working in Hadoop big data environments

• Strong understanding of data quality concepts, methodologies, and best practices.

• Experience with data quality tools and technologies

• Experience with Data Management

• Strong collaboration skills and ability to work effectively in a cross-functional, interdependent team environment

• Motivation, creativity, self-direction, and desire to thrive on small project teams

• Keen sense of prioritization and ability to time

• Superior academic record with a degree in a technical field

• Strong written and verbal English communication skills

• Eager to experiment with new team processes and innovate on testing approach

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.