Job Description
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.
Job Tags
Full time, Remote work