Data QA Engineer/ 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.

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