Senior Data Quality Analyst I

Description and Requirements

• Position Summary
• MetLife established a Global capability center (MGCC) in India to scale and mature Data & Analytics, technology and operations capabilities in a cost-effective manner and make MetLife future ready. The center is integral to Global Technology and Operations with a focus to protect & build MetLife IP, promote reusability and drive experimentation and innovation. The Data & Analytics team in India mirrors the Global D&A team with an objective to drive business value through trusted data, scaled capabilities, and actionable insights. The operating models consists of business aligned data officers- US, Japan and LatAm & Corporate functions enabled by enterprise COEs- data engineering, data governance and data science
• Role Value Proposition:
• Data Quality Analyst plays a critical role in data and analytics life cycle and significantly contributes to ensuring data quality of production grade data and analytics solutions. The role requires one to demonstrate expertise in data profiling, validation, data pipeline monitoring and quality reporting.This role mentors Data Quality Analysts. It is an individual contributor role, expected to solve wide ranging business problems, It is an individual contributor role.
• Job Responsibilities
• Build and maintain validation rules/test cases to ensure quality of data on variety of dimensions: accuracy, completeness, consistency and timeliness • Perform DQ checks at various lifecycle stages e.g. (unit testing , intergration testing, user acceptance testing) • Perform profiling to detect anomalies • Create and maintain data quality artefacts • Collaborate with multiple partners from Business, Technology, Operations and D&A capabilities (Data Governance, BSA, Data science, Data Platforms, BI) • Independently lead design, solutioning & estimations • Mentor DQ Analysts • Collaborate with multiple partners from Business,Technology , Operations and D&A capabilities (Data Governance, Data Quality, Data Modeling, Data Architecture, Data science, DevOps, BI & insights)