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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.

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