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.

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