Lead Data Quality Engineer

Job Description

Lead Data Quality Engineer Job Description The Services organization is a key differentiator for Mastercard, providing cutting-edge products that help our customers grow. Focused on thinking big and scaling fast around the globe, this team is responsible for end-to-end solutions for a diverse global customer base. Centered on data-driven technologies and innovation, these services include payments-focused consulting, loyalty and marketing programs, business Test Learn experimentation, and data-driven information and risk management services. We are the global technology company behind the world s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless . We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. Lead Data Quality Engineer As a Lead Data Quality Engineer, you will have the chance to tap into your expertise and knowledge to champion data quality by crafting and deploying testing at scale. This role requires a deep understanding of data quality principles, quality assurance methods, and the ability to collaborate with cross-functional teams to identify and resolve data quality issues. At the same time, you will increase data reliability and consistency across complex datasets and communicate the health of the data pipeline to varying levels of analysts and partners in the organization. Someone in this role is expected to implement proactive data quality strategies which are both scalable and maintainable. 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