What are the responsibilities and job description for the Remote Data Quality Analyst position at Easy Recruiter?
About the job Remote Data Quality Analyst
Description About Your team We are growing our enterprise data team of analysts, architects and engineers. This team is responsible for data governance, business intelligence, enterprise data architecture, data analytics, and ensuring that data is managed as a core corporate asset at OReilly. As a member of the enterprise data team, you will work collaboratively with engineering, product, security, data science, legal, and individuals throughout all areas of the business to ensure reliability, quality, and trustworthiness of data used for business decision making.
About The Job As a Data Quality Analyst you will be responsible for helping OReilly ensure the reliability of our enterprise data. Data is one of our most important assets, and as a member of our data team youll be responsible for helping us manage it. We are looking for a candidate who not only has the data analysis skills to successfully investigate, analyze and respond to day to day business issues, but also someone who has the ideas, experience and desire to help us build proactive solutions that will help us improve and grow the reliability, consistency and quality of our data platform as our company grows.
You Will Investigate and manage questions and incidents related to enterprise data for root cause analysis
- Work with engineers to provide recommendations on solutions to existing data quality issues
- Design, build and monitor key quality metrics dashboards, alerts and other monitoring tools
- Implement proactive anomaly detection to identify potential data streams with errors; covering both the known and unknown
- Collaborate on the design of a data quality framework
- Work with data architects, product managers, engineers and business intelligence analysts to understand and define clear data definitions and conventions across the organization
- Work with product managers and architects on product changes to help ensure an accurate understanding of the use and impact of data to ensure quality and reduce risk
- Work directly with end users to define, understand and help solve questions and problems around data
- Documentation of data classification, definitions and requirements across all data domains
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