Quality Assurance Engineer

Anika Systems is seeking a highly technical Quality Assurance Engineer with strong development, SQL, and Python expertise to support enterprise data platforms for federal clients. This is not a traditional manual QA role and this position requires a developer mindset, focused on automation, data validation, and platform reliability across modern cloud-based architectures. The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures such as materialized views—ensuring data accuracy, integrity, and trust across the enterprise. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve quality and delivery speed. This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering 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. Data Validation & ETL Testing 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. Iceberg & Materialized View Testing Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including: Schema evolution validation Time travel and versioning accuracy Partitioning and performance behavior Validate and compare materialized views vs. Iceberg table performance and consistency, including: Query performance benchmarking Data freshness and latency Storage efficiency and maintenance overhead Ensure alignment between precomputed datasets (materialized views) and underlying source data. Data Quality, Metadata & Context Validation Implement automated validation for data quality rules, lineage, and metadata accuracy. Support context engineering by validating that datasets include proper business context, definitions, and relationships. Integrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trust. Validate AI-generated metadata, lineage, and transformations for accuracy and traceability. AI-Driven Quality Engineering Apply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysis. Validate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standards. Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows. Ensure alignment with responsible AI practices, including traceability, explainability, and data integrity. OCDO & Data Strategy Support Support enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systems. Contribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherence. Align QA processes with Federal Data Strategy and Evidence Act requirements. Stakeholder Collaboration & Agile Delivery Work closely with data engineers, data architects, and analysts to define test strategies and acceptance criteria. Participate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain points. Document test results, defects, and quality metrics for both technical and non-technical stakeholders. Operate within Agile teams to iteratively improve data quality processes and tooling. Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows. Required Qualifications 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.) Apache Iceberg or similar data lake technologies 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. Preferred Qualifications Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System. Familiarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow). Experience with Apache Spark or distributed data processing frameworks. Knowledge of data quality tools and observability platforms. Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI). Experience testing large-scale cloud data platforms and lakehouse architectures. Experience validating data pipelines supporting AI/ML, analytics, or generative AI solutions. Familiarity with AI-driven testing tools or frameworks.