Back
Early-career, common QA title and generalist requirements increase applicant density despite smaller company brand.
SQL, ETL and data QA skills are broadly transferable across industries.
No explicit years but mandatory SQL/ETL and data-quality skills impose moderate filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Support testing and quality assurance for data pipelines, ETL processes, data quality, reports, and dashboards to ensure accurate data delivery.
Execute test cases, validate data mappings, perform data reconciliation, and verify data accuracy using SQL.
Document defects and data issues, assist in creating test plans, and participate in UAT, regression, and release validation activities.
Basic understanding of SQL and relational databases.
Knowledge of ETL/ELT concepts, data pipelines, and data transformations.
Ability to validate data quality, reports, dashboards against business requirements.
Work Experience Required: Junior or early career level (experience explicitly not mentioned).
Detail-oriented graduate or early-career professional with an interest in data and data quality.
Comfortable investigating data issues and understanding business use of data for decision-making.
Familiarity with data QA processes and motivation to build a career in Data Quality, Data Engineering, Analytics, or Data Science QA.