Quality Assurance Engineer

About the Company

Anika Systems is a leading technology solutions provider specializing in enterprise data management, analytics, and cloud-based architectures. Committed to delivering innovative solutions for federal clients, Anika Systems focuses on enhancing data quality, integrity, and accessibility across complex government agencies. With a strong emphasis on automation, AI integration, and modern data platform development, the company strives to support mission-critical initiatives that require high reliability, security, and compliance. Anika Systems values diversity, innovation, and excellence, fostering a collaborative environment that encourages continuous learning and professional growth.

About the Role

We are seeking a highly technical and experienced Quality Assurance Engineer to join our team remotely. This role is pivotal in ensuring the quality, reliability, and performance of enterprise data platforms supporting federal agencies. Unlike traditional manual QA roles, this position emphasizes automation, development skills, and data validation techniques. The ideal candidate will possess a developer mindset, with expertise in Python, SQL, and cloud data technologies, to design and implement automated testing frameworks for complex data workflows, including ETL pipelines, Apache Iceberg data lake architectures, and financial datasets such as XBRL. The role also involves leveraging AI tools to enhance testing processes, improve coverage, and accelerate delivery timelines. Collaboration with cross-functional teams, including data engineers, architects, and AI specialists, is essential to align testing strategies with organizational objectives and compliance standards. This is an excellent opportunity for a professional passionate about data quality, automation, and innovative testing methodologies to make a significant impact within a federal-focused environment.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
• Minimum of 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 such as S3, Glue, Redshift, and Lambda
• Familiarity with Apache Iceberg or similar data lake technologies
• Experience validating materialized views and performance-optimized data structures
• Knowledge of XBRL or complex financial/regulatory datasets
• Understanding of data modeling, metadata, and data governance principles
• Experience with CI/CD tools and automation integration
• Proficiency with AI tools and AI-assisted testing workflows
• Understanding of data quality requirements for AI/ML and analytics applications
• U.S. Citizenship and ability to obtain and maintain a federal clearance

Responsibilities

• 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 processes
• Develop comprehensive 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 enhance test coverage
• Validate ETL/ELT pipelines to ensure accurate data ingestion, transformation, and delivery
• Create automated checks for data completeness, accuracy, consistency, and timeliness
• Test ingestion and transformation processes involving complex datasets such as XBRL financial data
• Implement reconciliation and audit mechanisms across source-to-target data mappings
• Apply AI-driven anomaly detection techniques to identify data quality issues and pipeline failures
• Develop and execute testing strategies for Apache Iceberg-based data lakehouse architectures, including schema evolution validation, versioning, and performance benchmarking
• Validate and compare the performance and consistency of materialized views versus source data
• Implement automated validation for data quality, metadata, lineage, and contextual information
• Support context engineering by ensuring datasets include proper business definitions and relationships
• Integrate QA processes with enterprise data catalogs and metadata management systems
• Utilize AI/ML and generative AI tools to enhance testing processes, defect prediction, and root cause analysis
• Collaborate with data and AI teams to ensure data readiness for AI/ML and generative AI workflows
• Support enterprise data management programs and contribute to data maturity assessments aligned with federal standards
• Engage with stakeholders to define testing strategies, acceptance criteria, and report on quality metrics
• Participate in Agile teams, promoting automation and AI-driven efficiencies within data quality processes

Benefits

• Competitive salary and comprehensive benefits package
• 100% remote work environment with flexible scheduling
• Opportunities for professional development and continuous learning
• Engagement in impactful federal projects supporting national priorities
• Collaborative and innovative work culture
• Access to cutting-edge tools and technologies in data and AI domains

Equal Opportunity

Anika Systems is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We promote fairness and equal opportunity in all aspects of employment and are dedicated to fostering a workplace that reflects the diverse communities we serve.