Data QA Engineer

UTS Global

Required skills

Website: uts-global.com
Job details:

Key Responsibilities:

Validate end-to-end ETL workflows, data transformations, and data movements across multiple systems.
Perform data quality checks, including completeness, consistency, integrity, duplication, and reconciliation.
Write and execute complex SQL queries to validate large datasets.
Conduct source-to-target (S2T) mapping validation and data lineage verification.
Design, develop, and execute manual and automated data validation test cases.
Identify, analyze, and document data defects, inconsistencies, and anomalies.
Collaborate with engineering teams to understand data requirements, schemas, and business rules.
Test across data warehouses, data lakes, reporting layers, and BI dashboards.
Support performance testing of ETL jobs to ensure scalability and reliability.
Participate in CI/CD and automation pipelines for continuous data quality.
Maintain test coverage documentation, test plans, and QA standards for data validation.

Qualification:

3–7 years of experience as a Data QA / ETL Tester / Data Validation Engineer.
Strong command of SQL, including complex joins, aggregations, window functions, and stored procedures.
Hands-on experience with ETL testing tools and frameworks (e.g., Informatica, Talend, DataStage, ADF, Glue — as applicable).
Experience validating data pipelines in cloud platforms (AWS / Azure / GCP preferred).
Solid understanding of data warehousing concepts (staging, fact/dimension tables, SCDs, partitioning).
Familiarity with file formats such as CSV, JSON, Parquet, Avro, XML.
Experience with big data ecosystems (Spark, Hadoop, Databricks) is a plus.
Knowledge of Python/Scala for automation is an advantage.
Strong analytical, debugging, and problem-solving skills.
Hands-on experience in using tools like Jira, Git, Jenkins, Postman, etc.

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