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Customer Success Data WranglerUnited States


Customer Success Data Wrangler

United States

Qventus is looking for a self-determined individual who will fearlessly defend the integrity and quality of our data, which serves as the backbone Qventus' company vision. This person will take the lead on data operations activities, using a combination of the following experience to maximize the value on the data we consume.

The Data Wrangler will directly support every aspect of our data platform, with the end goal of enabling data to be consumed for a variety of downstream purposes. Candidates should be excited by the prospect of solving mysteries related to unexpected data or data science outputs, designing tools and approaches to make our data platform more resilient and error proof, and generally jumping into data topics wherever they pop up. He/she will also collaborate with our engineering and data science teams to suggest ways of using data we receive in novel ways. Though primarily wrangling data, this role will also support other efforts of the CSOps team, which strives to create tools and processes that will enable our team and company to scale.

The ideal candidate will have
    • 3-5 years direct work experience with data mining, munging, cleansing, and troubleshooting.
    • Expert level proficiency in SQL with at least 5+ years experience of complex querying in a professional setting.
    • 1-3 years of practical experience designing and developing database schemas
    • A proven ability to track and trace data through a pipeline/multiple steps/ETL processes
    • Experience with developing and executing QA scripts to for testing and validation
    • Ability to learn quickly with minimal supervision and identify "gaps" in the data
    • A basic working knowledge of data science and the ability to support a data science team.

This will role will involve, but is not limited to, the following responsibilities
    • End to end troubleshooting and resolution of data quality issues
    • Execution of data validation scripts on new data extracts and data feeds during deployment
    • Ongoing data integrity monitoring and maintenance across all database stacks.
    • Documentation and version management of schema changes across all database stacks.
    • Direct collaboration with Engineering and Product to operationalize and execute database schema releases across deployments
    • Design and development of data validation rules for the purpose of QA
    • Monitoring and maintenance of consistent data formats across deployments
    • Mapping "raw" data into another format with the intent of making it more appropriate and valuable for various consumers of the data

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