Data Quality Engineer

Responsibilities

• Design and Develop Data Testing Solutions
• Build, maintain, and optimize automated data testing frameworks and validation pipelines that support enterprise reporting, analytics, and business applications using SQL, Informatica, IICS, Snowflake, and Python.
• Develop and execute data validation routines for extracts, transformations, and reporting datasets to ensure completeness, accuracy, consistency, and reliability of enterprise data assets.
• Design automated reconciliation processes between source and target systems, including row count validation, schema validation, transformation testing, and data profiling.
• Partner with data engineering teams to embed testing and quality controls into ETL/ELT pipelines and CI/CD deployment processes across Snowflake, Oracle, and AWS environments.
• Leverage AI-assisted development tools and intelligent automation techniques to improve test coverage, accelerate validation processes, and enhance the efficiency of data quality engineering practices across enterprise data platforms.
• Support and contribute to enterprise test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
• Ensure compliance with enterprise data governance, security, and regulatory requirements by implementing data quality standards, monitoring controls, and audit-ready validation processes.
• Work with structured and semi-structured data formats (XML, JSON) and cloud-native services to validate data ingestion, transformation, and integration processes across distributed platforms.
• Collaborate with data engineers, analysts, QA teams, and business stakeholders to define testing requirements, improve data quality processes, and support reporting solutions such as Power BI.
• Recommend and implement improvements to data quality frameworks, testing automation, monitoring solutions, governance processes, and Data Ops practices.
• Mentor junior team members and promote best practices in data quality engineering and testing.

• Bachelor’s degree in Computer Science, Information Systems, or a related field; equivalent work experience considered.
• 6+ years of experience in data engineering, data testing, or database development.
• Demonstrated expertise in SQL development and query tuning.
• Automated data testing and validation methodologies.
• Informatica and IICS for ETL and data integration testing.
• Snowflake data warehouse architecture and validation.
• Oracle database systems.
• Data reconciliation and data profiling techniques.
• Data modeling, normalization, and relational design.
• Handling and validating XML and JSON data structures.
• Building data quality solutions in AWS cloud environments.
• Python-based automation and testing frameworks.
• Strong knowledge of test environment strategy, including environment planning, test data management, deployment coordination, integration testing support, and validation across development, QA, UAT, and production environments.
• Experience establishing and supporting end-to-end test strategies for enterprise data pipelines and distributed data platforms.
• Understanding of environment dependencies, release validation processes, and data synchronization considerations for large-scale data ecosystems.
• Experience developing automated test scripts and reusable validation frameworks.
• Strong understanding of ETL/ELT testing methodologies and end-to-end data flow validation.
• Strong problem-solving abilities and the capacity to work independently on complex technical challenges.
• Deep understanding of data security, governance, compliance, and data quality best practices.
• High degree of self-motivation, intellectual curiosity, and commitment to continuous improvement.