Position: Data QA Engineer
Company: BrickRed System
Experience required: 1y
English level: intermediate

Responsibilities:
 Design, build, execute, and maintain automated tests and/or manage deep data profiling runs to
ensure data products and pipelines meet expectations
 Support business analysts by providing technical insights to close gaps in understanding
 Work collaboratively with analysts, engineers, subject matter experts, and engagement
managers to apply analytical methods to satisfy client needs
 Participate in the growth of the DQE practice by sharing knowledge and lessons learned,
continually improving best practices, and contributing to methods that will systematically
advance workforce capabilities
 Contribute to building a Universal Workplace, where cognitively diverse teams can thrive, and
model the organization’s positive, supportive culture of respect and excellence
Requirements:
 Direct experience with any or all of the following: data quality, data management, data
governance, analytics, business intelligence, dashboards or reporting, data science, and/or data-
intensive software development projects (including exposure to and familiarity with the
technologies and tools)
 Foundational skills with at least two from: R, R Markdown, Python, and/or SQL
 Demonstrated familiarity or experience with navigating data ecosystems (data warehouses, data
lakes, structured and unstructured data, file types, BI systems) and familiarity with terms and
concepts therein
 Ability to effectively coordinate and collaborate with data quality engineers, launch team
members, and other internal subject matter experts to successfully leverage and continually
improve tools, processes, and resources
 Aptitude for becoming familiar with new tools and technologies
 Ability to work independently as well as in a remote, team environment; ability to work
effectively and collaboratively within your team, participating in SCRUM/agile and other
meetings
 Ability to communicate frequently and openly with leads and team members (non-speaking
team members can communicate via text)

Preferred:
 Exposure to, and ability to work with, a variety of data formats and storage mechanisms (e.g.
JSON, CSV, XML, Bigquery, GCS, Redshift, Dynamo, S3, kdb+)
 Python/Great Expectations (highly desired)
 R Shiny or R in production
 Basic proficiency with SQL and exposure to NoSQL databases
 Familiarity with BI systems like Tableau and PowerBI
 Familiarity with data management infrastructure elements like Databricks, Airflow, TMM, or
other pipeline management systems.