Data QA Engineer

Responsibilities

• Take ownership of end to end data quality

  • Understand and Contribute to the event model design

  • Build and automate testing frameworks around data ingestion pipelines.

  • Write complex SQL queries on tables with hundreds of millions of records and ensure data integrity is maintained throughout the ETL lifecycle.

  • Design test cases and write python/SQL scripts to validate data integrity and identify gaps and opportunities in our pipelines.

  • Track data issues and work with team leads from discovery to resolution.

• Collaborate with the analytic teams to conduct data quality investigations, improve automation and tools.

• Review current tools and enhance them to help with data integrity.

Minimum Qualifications

• 5+ years of work experience in QA, preferably in data or relevant space

• Demonstrable knowledge, experience, skill, and proficiency with the following:

  • Scrum/Agile methodologies

  • SDLC

  • Python (at least reading)

  • SQL

• Experience with different facets of QA tests such as functional progression & regression, integration, performance, load, UAT, and operational readiness testing

• Must be self-motivated, able to work independently, and thrive in a fast-paced, multi-tasking, high productivity environment while maintaining excellent working relationships with people in a wide variety of functional areas

• Excellent verbal and written communication skills
   

Preferred Qualifications

• Applied experience with Databricks and/or Azure ML

• Strong coding abilities in one or more scripting languages like Python or SQL

• Understanding of compliance, security, and risk domains along with associated patterns and data elements

• Use of one of the following vendor reporting solutions: PowerBI or Tableau

• Understanding of product and services activation, use, and transaction models and data

• Understanding of statistical analysis and machine learning tools and practices

• Understanding of Cloud-centric data processing and visualization approaches including SQL and NoSQL databases with exposure to Azure SQL, Azure Cosmos DB, Data Factory, Synapse, Azure Data Lake, etc

• Familiarity with Agile software delivery including application lifecycle mgmt (Jira/Azure DevOps/VSTS, Git).

All your information will be kept confidential according to EEO guidelines.