Senior Quality Engineer

performing data analysis and validation, collaborating with software developers to enhance data
products, and identifying areas for improvement and optimisations within the data products. Data
Quality Analyst/ Executive responsibilities include designing and implementing tests, debugging
and defining corrective actions. The Data Quality Analyst/ Executive role plays an important part
in the development of Data products within Cimpress. Our ideal candidate will be responsible for
conducting tests before product launches to ensure data passes all quality checks and meets
business needs. If you hold an engineering background and enjoy problem solving and root
cause analysis for data quality problems, we d like to meet you.
What should you expect as an employee of Cimpress
Truly global and open culture
Collaborative workplace with bunch of brilliant minds as your colleagues, peers and
coworkers
Sponsorship for entrepreneurial mindset and high ownership
Flexible work timings to help interact and align with global stakeholders
Mentorship from some of the best engineering minds across the globe
Tremendous opportunities for learning new technologies
Encouragement for innovation and creativity
Competitive healthcare benefits and many more.
What are you expected to do

Proactively improve the quality of data products through manual and automated
data validation.
Report data analysis and validation findings to developers and product team to
inform business decisions and prioritize requirements.
Analyze data and make recommendations to improve coverage and accuracy of
Customer Enrichment product.
Document validation processes and maintain data records.
Keep abreast of developments and trends in data quality analysis.
Identify, compare, and resolve data quality problems.
Evaluate large datasets for quality and accuracy.
Determine business impact level for data quality issues.
Work with Programmers to correct data quality errors.
Determine root cause for data quality errors and make recommendations for long-
term solutions.
Develop and execute data cleanup measures.
Define and ensure adherence to data quality standards.
Resolve all internal data exceptions in timely and accurate manner.
Analyze, query and manipulate data according to defined business rules and
procedures.
Evaluate data quality based on the defined rules and thresholds.
Design data quality rules and practices to establish the data quality management
process.
Monitor and control data quality
Work with cross-functional teams to ensure data quality throughout the product
development lifecycle