Iris Software – Software Development Engineer
Job Description
Be valued, be inspired, be your best.
At Iris Software, we invest in and create a culture where colleagues feel valued, can explore their potential, and have opportunities to grow.
Our employee value proposition (EVP) is about “Being Your Best” – as a professional and person. It is about being challenged by work that inspires us, being empowered to excel and grow in your career, and being part of a culture where talent is valued. We’re a place where everyone can discover and be their best version.
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Responsibilities
• Data Testing Strategy & Execution: Design, develop, and execute comprehensive test plans and test cases for data-centric applications, ETL processes, data warehouses, data lakes, and reporting solutions.
• SQL-Driven Validation: Utilize advanced SQL queries to perform complex data validation, data reconciliation, data integrity checks, and data quality assurance across various financial data sources.
• ETL Testing: Conduct thorough testing of ETL (Extract, Transform, Load) processes, ensuring data is accurately extracted, transformed according to business rules, and loaded correctly into target systems.
• Data Quality Assurance: Implement and monitor data quality checks, identify data discrepancies, anomalies, and inconsistencies, and work with development and business teams to resolve issues.
• Performance Testing (Data Focus): Contribute to performance testing efforts for data pipelines and database operations, ensuring optimal query and data load performance.
• Test Data Management: Create and manage robust test data sets for various testing phases, including positive, negative, and edge case scenarios.
• Defect Management: Identify, document, track, and re-test defects in data, collaborating closely with development and data engineering teams for timely resolution.
• Documentation & Reporting: Maintain clear and concise documentation of test plans, test cases, test results, and data quality reports. Provide regular status updates to stakeholders.
• Collaboration: Work effectively with business analysts, data architects, data engineers, and project managers to understand data flows, business requirements, and ensure data quality standards are met.
• Process Improvement: Proactively identify opportunities for process improvements in data testing methodologies and tools.
• Global Team Collaboration: Provide consistent overlap with EST working hours (until noon EST) to facilitate effective communication and collaboration with US-based teams.
