Senior Data QA Analyst

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.

SKILLS Must have

• 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 (hands-on experience with ETL pipelines, data warehouses, and data validation at scale).
• 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

• 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).