Quality Assurance Data Analyst

Paradigm Information Services, Inc.•Portland, OR

10d•$33 - $38•Remote

About The Position

Paradigm is seeking an experienced Quality Assurance Data Analyst to support complex data processing, validation, and quality assurance initiatives for a provider of technology-enabled legal and business services. The role focuses on ensuring the accuracy, completeness, and integrity of large datasets used in business-critical applications. The analyst works with data engineers, business analysts, project managers, and QA professionals to validate data imports, transformations, extracts, and database processes. The position involves investigating complex data issues, developing testing strategies, and confirming data meets rigorous quality standards. This is a 12-month contract assignment, estimated according to expected workload and scope; it may be extended or conclude earlier based on business needs. The role is remote and limited to candidates in Oregon, Idaho, or Nevada. Pay is $33.00–$38.00 per hour.

Requirements

Bachelor's degree in Computer Science, Information Systems, Business Analytics, or a related field, or equivalent professional experience.
At least 2 years of hands-on database testing experience, including developing and executing complex SQL queries and validating database processes.
Experience with Microsoft SQL Server and strong proficiency in T-SQL.
Experience analyzing and testing stored procedures, functions, triggers, and complex database queries.
Understanding of relational databases, data modeling, and database structures.
Working knowledge of ETL concepts, processes, and data validation techniques.
Proficiency with Microsoft Excel for data analysis, reconciliation, and reporting.
Experience interpreting business requirements and translating them into test cases.
Strong analytical, troubleshooting, and deductive reasoning skills.
Excellent written and verbal communication skills.
Ability to work independently, collaborate across teams, and manage multiple assignments with changing priorities.
Advanced knowledge of T-SQL, including complex joins, subqueries, correlated subqueries, IN, and EXISTS; common table expressions (CTEs), temporary tables, and views; NULL handling and data type considerations; primary keys, foreign keys, constraints, and defaults; transactions and error handling; indexing and query performance considerations; stored procedures, user-defined functions, and triggers; bulk data operations including BCP and BULK INSERT; and database testing, reconciliation, and data integrity validation.

Nice To Haves

Experience validating large-scale data migrations, imports, or conversions.
Background in data-intensive business applications or environments involving high-volume transactional records.
Prior experience as a QA Data Analyst, Database QA Analyst, SQL Data Analyst, or similar technical role.

Responsibilities

Validate the accuracy and integrity of data imports, updates, transformations, and extracts involving datasets ranging from hundreds to millions of records.
Develop, execute, and maintain SQL-based test queries to verify data accuracy, completeness, and consistency.
Analyze complex SQL queries, stored procedures, triggers, and functions to confirm database operations meet business and technical requirements.
Identify data discrepancies, investigate root causes, document defects, and verify corrective actions.
Test standard and exceptional data scenarios, including edge cases and anomalies.
Review business and technical requirements to identify gaps, inconsistencies, and potential data quality risks.
Collaborate with business analysts and other stakeholders to translate requirements into comprehensive test plans and test cases.
Develop and execute testing strategies for database processes, data migrations, and ETL operations.
Conduct peer reviews to ensure adherence to established quality standards.
Work with data engineers, QA team members, and project managers to troubleshoot issues and resolve defects.
Communicate testing results, project status, risks, and outstanding issues clearly and promptly.
Maintain accurate documentation, project records, and time reporting.
Manage competing priorities and meet project deadlines while maintaining accuracy and attention to detail.
Follow established development, testing, and Data Services procedures and quality standards.