As a 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 analysis and engineering, quality assurance methods, and the ability to collaborate with cross-functional teams to identify and resolve data quality issues.
- Conduct thorough data profiling and analysis to identify anomalies, inconsistencies, and inaccuracies in datasets
- Automate processes, data quality checks, and workflows to ease data validation processes across complex, interdependent data systems
- 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.
- Design and implement intuitive metrics that show stake holders the health of their data in an actionable format
- Conduct initial root cause analysis for data issues, collaborate with partners to clearly identify the issue, scope and impact, and path for research/solutioning
- 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
- 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
Ideal Candidate Qualifications:
- 3+ 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.
- Strong collaboration skills and ability to work effectively in a cross-functional, interdependent team environment
- 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
