Quality Assurance Engineer

Anika Systems

Quality Assurance Engineer focusing on automation and data validation. Requires Python, SQL skills supporting enterprise data platforms for federal clients.

Posted 6/30/2026full-timeRemote • 🌎 Anywhere in the WorldMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftApacheAWSETLPythonSQL

About the role

Key responsibilities & impact

Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
Build reusable testing utilities for data validation, regression testing, and pipeline certification.
Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
Develop unit, integration, and end-to-end test cases for complex data workflows.
Leverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
Create automated checks for data completeness, consistency, accuracy, and timeliness.
Test ingestion and transformation of complex datasets, including XBRL financial data.
Implement reconciliation and audit mechanisms across source-to-target mappings.
Apply AI-driven anomaly detection to identify data quality issues and pipeline failures.
Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures.
Ensure alignment between precomputed datasets (materialized views) and underlying source data.
Implement automated validation for data quality rules, lineage, and metadata accuracy.
Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
5+ years of experience in QA engineering, data testing, or software development.
Strong programming skills in Python and advanced proficiency in SQL.
Experience building automated test frameworks for data platforms and ETL pipelines.
Hands-on experience with: AWS data services (S3, Glue, Redshift, Lambda, etc.)
Experience validating materialized views and performance-optimized data structures.
Familiarity with XBRL or complex financial/regulatory datasets.
Understanding of data modeling, metadata, and data governance principles.
Experience with CI/CD tools and automated testing integration.
Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
Understanding of data quality requirements for AI/ML and analytics use cases.
U.S. Citizenship required; ability to obtain and maintain a federal clearance.

Benefits

Comp & perks

100% remote work
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Automated QA FrameworksData ValidationETL Pipeline TestingUnit TestingIntegration TestingEnd-to-End TestingData Quality RulesData GovernanceAnomaly DetectionData Modeling

Soft Skills

CollaborationDocumentationCommunication