Data Quality Engineer
First seen by Alion on Oct 1, 2026.
Key Responsibilities :
- Independently write complex SQL queries in Snowflake to validate analysis findings and reconcile outputs with source data.
- Perform end-to-end data-quality validation across source, transformation, and target layers.
- Develop and execute dbt schema tests and data tests, including uniqueness, not-null, referential-integrity and accepted-value validations.
- Identify duplicates, invalid or expired records, inconsistencies, missing data and false positives in detection logic.
- Perform detailed reconciliation using row counts, aggregates, source-to-target comparisons and edge-case validations.
- Validate data pipelines, transformation logic, analytical datasets and downstream outputs.
- Investigate data-quality issues, identify root causes and work with data engineering/analytics teams on resolution.
- Maintain clear evidence and documentation of test scenarios, findings, reconciliation results and defects.
- Where applicable, validate AI/ML or LLM-generated outputs by sampling against ground truth, measuring accuracy, identifying failure patterns and checking consistency across repeated runs.
- Support data lineage, traceability and data-cataloguing requirements.
Required Skills :
- 3-7 years of relevant experience in data testing, data validation, data engineering QA or analytics quality assurance.
- Strong hands-on SQL skills with the ability to independently investigate and reconcile data.
- Practical experience working with Snowflake.
- Hands-on experience with dbt testing, including schema tests, data tests and assertions.
- Strong understanding of data-quality dimensions and validation techniques.
- Experience identifying duplicates, invalid/expired records, inconsistencies and false positives.
- Strong reconciliation skills, including source-to-target validation, row counts, aggregates and edge cases.
- Experience testing data pipelines and analytics outputs, rather than primarily UI/application testing.
Preferred Skills :
Experience validating AI/ML or LLM-generated results is strongly preferred, particularly ground-truth comparison, accuracy measurement, failure-pattern analysis and consistency testing. Familiarity with Snowflake tooling and exposure to data cataloguing, lineage and traceability will also be valuable.
Knowledge of supply chain, ERP/SAP or regulated data environments is an advantage, especially the ability to identify outputs that appear technically plausible but are incorrect from a business perspective. Familiarity with Dagster or similar orchestration tools is also desirable.
Skills
Data Quality, Data Profiling, Data Validation, Data Engineering, Data Analytics, SQL, Snowflake DB, Data Build Tool, Data Lineage, Data Catalogue