Senior Manager, Data Quality

Role Summary

The Senior Manager, Data Quality is responsible for designing and implementing comprehensive frameworks to ensure the accuracy, consistency, and reliability of organizational data across systems. This role involves proactively identifying and resolving data discrepancies, establishing robust policies and standards for data validation, cleansing, and monitoring, and collaborating with cross-functional teams to align data quality efforts with business objectives. By leveraging advanced tools, machine learning algorithms, and innovative methodologies, the Senior Manager drives continuous improvement in data quality governance, delivering actionable insights and optimizing data processes to support organizational growth and decision-making.

Responsibilities

• Design and execute Data Quality frameworks to ensure the accuracy, consistency, and reliability of organizational data across systems.
• Proactively profile data, identify, track, and resolve data discrepancies or errors, ensuring timely corrective actions.
• Establish and enforce policies and standards for data quality, including processes for data validation, cleansing, and monitoring.
• Collaborate with IT functions, MDM, analysts, and business units to identify data quality needs and align efforts with organizational goals.
• Define and establish metrics to assess data quality performance and drive continuous improvement.
• Leverage advanced tools and strategic methodologies to deliver actionable insights and high-level recommendations for optimizing and transforming data processes.
• Research, select, and deploy Regeneron technologies to enhance data quality and governance capabilities.
• Maintain expertise in data quality industry trends and strategically implement advanced methodologies to elevate data quality governance practices.

Qualifications

• Required: BS/BA Degree and 8+ years relevant experience. MS degree and 6+ years relevant experience can be considered.
• Knowledge Requirements:
• Required: Knowledge of data modelling techniques (e.g., conceptual, logical, physical models).
• Required: Experience with modern database technologies such as PostgreSQL, Snowflake
• Required: Familiarity with ETL/ELT processes and data pipelines
• Required: Understanding of machine learning and AI algorithms that can identify, predict, and resolve data quality issues.
• Required: Ability to leverage AI for anomaly detection, pattern recognition, and automated data correction.

Skills

• Required: Strong coding skills in languages such as Python, SQL, R, or Java for building custom DQ solutions.
• Required: Experience with APIs and microservices architecture for integrating DQ tools with other systems.
• Required: Ability to design algorithms and workflows that automate data quality checks and monitoring.
• Required: Advanced data analysis, profiling, and validation techniques.
• Required: Ability to design and monitor data quality metrics and KPIs.
• Required: Problem-solving and critical-thinking skills for complex challenges.

Education

• BS/BA Degree required; MS degree with related experience considered.

Additional Requirements

• Preferred: Familiarity with tools for data quality management (e.g., Informatica)
• Preferred: Understanding of cloud platforms (e.g., AWS, Snowflake)
• Preferred: Knowledge of data visualization tools (e.g., Tableau) that communicate insights on DQ issues effectively
• Preferred: Travel up to 25% may be required