* Project Description:We are seeking a meticulous and analytical Data QA Analyst to join a new data team working with Large Language Models. You’ll play a critical role in ensuring the accuracy, consistency, and reliability of our data. To ensure success as a data QA engineer, you should have programming skills and a keen eye for detail. Successful candidates will be evidently enthusiastic and motivated people who we can train up in our processes and ultimately play a key role in quality assurance initiatives across different stakeholder groups. * Responsibilities:• Develop and execute test plans, test cases, and scripts for data validation across ETL processes, databases, and reporting tools. • Perform root cause analysis on data issues and work with engineering and analytics teams to resolve them. • Monitor data quality metrics and implement automated checks to detect anomalies. • Validate data transformations, aggregations, and business logic in dashboards and reports. • Collaborate with data engineers, analysts, and product managers to define QA requirements and acceptance criteria. • Document QA processes, test results, and data issue logs for transparency and continuous improvement. * Mandatory Skills Description:• Proven experience in data QA, data analysis, or data engineering roles. • Experience with MS SQL and PostgreSQL • Strong SQL skills for querying and validating large datasets. • Familiarity with data warehousing concepts and ETL processes. • Understanding of data governance, data lineage, and metadata management. • Excellent attention to detail and problem-solving abilities. • Strong communication skills to explain data issues and collaborate with cross-functional teams. • Scripting and automation (e.g., PowerShell, Python, Java). • Experience with Gitlab. • Knowledge of Spotfire data visualization platform or alternative dashboard solutions. • Awareness of Agile delivery methodologies. * Nice-to-Have Skills Description:• Experience with cloud-based database solutions. • Understanding of data lifecycle management and SOC2 security standards. • Familiarity with geoscience disciplines, geospatial data and GIS tools (e.g., ArcGIS, QGIS) is advantageous. • Experience with Python or other scripting languages for automated testing. • Familiarity with cloud data platforms (e.g., Snowflake, BigQuery, AWS Redshift). • Knowledge of data quality frameworks and tools (e.g., Great Expectations, dbt tests). * Languages: * English: B2 Upper Intermediate
