Data Quality Engineer
Core
Design, implement, and optimize enterprise data validation frameworks to ensure the accuracy, reliability, and integrity of business-critical data solutions for analytics, reporting, and operational systems.
Role type
Senior Data Quality Engineer (Data Testing & Validation)
Builds
Scalable data validation frameworks, automated testing strategies, and reconciliation processes for enterprise data pipelines and platforms.
Domain
Insurance industry data engineering and quality assurance
Deliverable
production ML models | product features | dashboards & analysis | infrastructure
Required skills
SQL development and query tuning, automated data testing and validation methodologies, Informatica and IICS for ETL testing, Snowflake data warehouse architecture, Oracle database systems, data reconciliation and profiling, data modeling and relational design, XML and JSON validation, AWS cloud environment data quality solutions, Python-based automation frameworks
Preferred skills
Data Vault 2.0 methodologies, data quality and observability tools, PowerShell scripting, Git and CI/CD pipelines, Spark, Kafka, Airflow, DBT, Infrastructure as Code, automated monitoring and anomaly detection, DevOps and DataOps practices, Power BI reporting validation, AI-assisted development and testing tools
Technologies
SQL, Informatica, IICS, Snowflake, Oracle, AWS, Python, XML, JSON, Power BI, Spark, Kafka, Airflow, DBT
Responsibilities
Design and develop automated data testing frameworks and validation pipelines; execute data validation routines for extracts, transformations, and reporting datasets; design automated reconciliation processes between source and target systems; partner with data engineering teams to embed testing controls into ETL/ELT pipelines and CI/CD processes; leverage AI-assisted development tools to improve test coverage; support enterprise test environment strategy; ensure compliance with data governance and regulatory requirements; collaborate with stakeholders to define testing requirements and improve data quality processes; recommend and implement improvements to data quality frameworks and DataOps practices
Seniority
Senior, hands-on IC with mentorship responsibilities
