Data Quality Engineer

Data integrity is crucial to our operations and decision-making processes. The Data Quality Engineer is a vital resource in ensuring that our data is accurate, complete, and reliable. The ideal candidate for this role is a proactive individual who takes ownership of quality assurance initiatives and delivers high-standard data validation solutions. The primary responsibilities include validating data from various sources, ensuring accuracy through comprehensive testing, and identifying patterns and outliers within datasets. Data Quality Engineers play a significant role in maintaining the trustworthiness of data assets across the organization.

Position Summary

The Data Quality Engineer will contribute to building and executing Chubb's information strategy within Personal Risk Services. This individual will collaborate with key stakeholders, data engineers, data analysts, and report developers to ensure that high-quality data is consistently delivered. The role provides a unique opportunity to work with Chubb data and gain insights into the Personal Insurance domain, focusing on data quality and integrity.

Responsibilities

Validate data from source to target by performing thorough checks and assessing consistency across datasets.

Develop and execute automated tests to confirm data accuracy and detect anomalies, ensuring that data quality standards are met.

Analyze and document data quality issues and trends, providing recommendations for remediation and improvement.

Collaborate with data analysts and engineers to define data mappings, transformations, and rules for data quality verification.

Review data processing workflows and implement quality controls to ensure only high-quality data is utilized in business operations.

Desired Qualifications

Expert-level proficiency in Structured Query Language (SQL) for data validation and analysis.

Prior experience working in Data Warehousing or ETL Teams.

Strong knowledge of Python and experience in using test automation tools for data quality assessments.

Familiarity with various relational database management systems but not limited to SQL Server. 

Experience testing with flat files, CSV, and semi-structured data formats like JSON and XML.

Solid understanding of data testing principles and best practices for data quality management.

Bonus Skills

Experience in the Property and Casualty Insurance domain or similar industries.

Familiarity with data profiling techniques and tools to assess data quality metrics.

Exposure to cloud platforms such as Microsoft Azure or AWS.

Experience with version control systems like Git and integrated development environments.

Understanding of Agile development principles and methodologies, such as SAFe Agile.

This position is essential for maintaining the integrity and reliability of our data assets, ensuring that our strategic decision-making is based on accurate and trustworthy information.