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QA Data Engineer



Data Science, Quality Assurance
United States
Posted on Wednesday, June 5, 2024


Data Engineers serve a unique and important role in daily operations at Wider Circle. Customer data is the bedrock of our business, and Data Engineering is responsible for laying the foundation for our success. Data Engineers work with internal and external stakeholders to gather, validate, clean and move data inside and outside the organization using technology and automation. Our data engineering team is also responsible for quality curation of data to ensure our products are released on time and with minimal errors and/or bugs.

You will be joining a talented, fully remote Data Science, Engineering and Analytics team that handles a wide range of requests including customer data processing, weekly report automation, new product development and complex data integration.

Company Overview

At Wider Circle, we connect neighbors for better health. Wider Circle's groundbreaking Connect for Life® program brings neighbors together in-person and online for health, wellness, and social activities that improve mental and physical health. We create webs of community circles by employing local and culturally competent engagement specialists, whose hand-on-hand approach to forming trusted circles is informed by a sophisticated analytics platform. We are on a mission to make the world a better place for older adults and disadvantaged communities.


  • Develop and maintain data quality and accuracy dashboards, and scorecards to track data quality and model performance.
  • Develop, maintain, and enhance a comprehensive data quality framework that defines data standards, quality and accuracy expectations, and validation processes.
  • Enhance our data quality through rapid testing, feedback and insights.
  • Partnering with Engineering & Product to predict data quality issues and production flaws.
  • Conceptualize data architecture (visually) and implement practically into logical structures.
  • Performing testing of data after ingesting and database loading.
  • Manage internal SLAs for data quality and frequency.
  • Provide expert support for solving complex problems of data integration across multiple data sets.
  • Updating and evolving our data ecosystem to streamline processes for maximum efficiency.