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.This is not a UI Automation role. We require hands-on experience with ETL pipelines, data warehouses, and data validation at scale.
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.

• Minimum 1+ year of hands-on experience in ETL / DWH / data validation or data pipelines testing
• Experience with MS SQL and PostgresSQL
• 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).