[Remote] Quality Assurance Engineer

Note: The job is a remote job and is open to candidates in USA. 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. The role focuses on automation, data validation, and platform reliability across modern cloud-based architectures, requiring the design and implementation of automated testing frameworks and collaboration with data teams.ResponsibilitiesDesign, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQLBuild reusable testing utilities for data validation, regression testing, and pipeline certificationIntegrate automated tests into CI/CD pipelines to support continuous testing and deploymentDevelop unit, integration, and end-to-end test cases for complex data workflowsLeverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverageValidate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of dataCreate automated checks for data completeness, consistency, accuracy, and timelinessTest ingestion and transformation of complex datasets, including XBRL financial dataImplement reconciliation and audit mechanisms across source-to-target mappingsApply AI-driven anomaly detection to identify data quality issues and pipeline failuresDevelop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including: Schema evolution validation, Time travel and versioning accuracy, Partitioning and performance behaviorValidate and compare materialized views vs. Iceberg table performance and consistency, including: Query performance benchmarking, Data freshness and latency, Storage efficiency and maintenance overheadEnsure alignment between precomputed datasets (materialized views) and underlying source dataImplement automated validation for data quality rules, lineage, and metadata accuracySupport context engineering by validating that datasets include proper business context, definitions, and relationshipsIntegrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trustValidate AI-generated metadata, lineage, and transformations for accuracy and traceabilityApply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysisValidate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standardsCollaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflowsEnsure alignment with responsible AI practices, including traceability, explainability, and data integritySupport enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systemsContribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherenceAlign QA processes with Federal Data Strategy and Evidence Act requirementsWork closely with data engineers, data architects, and analysts to define test strategies and acceptance criteriaParticipate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain pointsDocument test results, defects, and quality metrics for both technical and non-technical stakeholdersOperate within Agile teams to iteratively improve data quality processes and toolingPromote adoption of AI-driven efficiencies and automation across QA and data engineering workflowsSkillsBachelor's degree in Computer Science, Engineering, Information Systems, or related field5+ years of experience in QA engineering, data testing, or software developmentStrong programming skills in Python and advanced proficiency in SQLExperience building automated test frameworks for data platforms and ETL pipelinesHands-on experience with: AWS data services (S3, Glue, Redshift, Lambda, etc.)Apache Iceberg or similar data lake technologiesExperience validating materialized views and performance-optimized data structuresFamiliarity with XBRL or complex financial/regulatory datasetsUnderstanding of data modeling, metadata, and data governance principlesExperience with CI/CD tools and automated testing integrationDemonstrated proficiency with AI tools and AI-assisted development/testing workflowsUnderstanding of data quality requirements for AI/ML and analytics use casesU.S. Citizenship required; ability to obtain and maintain a federal clearanceExperience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve SystemFamiliarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow)Experience with Apache Spark or distributed data processing frameworksKnowledge of data quality tools and observability platformsExposure to data maturity frameworks (e.g., EDM DCAM, TDWI)Experience testing large-scale cloud data platforms and lakehouse architecturesExperience validating data pipelines supporting AI/ML, analytics, or generative AI solutionsFamiliarity with AI-driven testing tools or frameworksCompany OverviewAnika Systems provides data and analytics, artificial automation, cloud engineering, application development & enterprise IT modernization. It was founded in 2005, and is headquartered in Leesburg, Virginia, USA, with a workforce of 51-200 employees. Its website is https://www.anikasystems.com/.