Anika Systems

QA Engineer

• 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.
• 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.
• Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures.
• Ensure alignment between precomputed datasets (materialized views) and underlying source data.
• Implement automated validation for data quality rules, lineage, and metadata accuracy.
• Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
• Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
• Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.

Quality Assurance Engineer

Data Engineer

• Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platforms
• Build scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasets
• Implement data transformation logic to support analytics, reporting, and regulatory use cases
• Ensure data pipelines are reliable, performant, and scalable in cloud environments
• Leverage AI-assisted development tools to accelerate pipeline development, testing, and optimization
• Develop and manage data solutions leveraging AWS services (e.g., S3, Airflow, DAGs, Glue, Lambda, Redshift)
• Implement and optimize Apache Iceberg table formats for large-scale, ACID-compliant data lakes
• Support lakehouse architectures that unify data lakes and data warehouses
• Optimize data storage and retrieval strategies for performance and cost efficiency
• Design and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS-native services
• Integrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational risk
• Ensure alignment with data governance frameworks and standards established by OCDO organizations, including AI data readiness and traceability
• Collaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutions.

• Lead the design and implementation of enterprise data architectures supporting federal clients
• Shape data strategy and enable data-driven decision-making
• Support the establishment and maturation of Office of the Chief Data Officer (OCDO) organizations
• Develop and maintain conceptual, logical, and physical data models, including Entity Relationship Diagrams (ERDs)
• Define and enforce standards for data integration, data quality, and data lifecycle management
• Lead or support stakeholder listening campaigns to gather input from executives, data leaders, and practitioners across the enterprise

Quality Assurance Engineer

Data Engineer

Data Architect