Data QA Engineer

Ex Parte, Inc. is a company focused on providing data and insights for legal decision-making. They are seeking a Senior Data QA Engineer to take ownership of data quality, automate testing frameworks, and ensure data integrity throughout the ETL lifecycle.

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

Requirements:

• 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
• 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)