Sr. QA Automation Engineer – Enterprise Data & AILocations: East Adelphi, MD 20783 (Hybrid 1-2 days a week) Duration: 12 Months with Possible Extension Rate Range: $30-35/Hour on W2 without BenefitsRole SummaryWe are seeking a QA Automation Engineer / SDET to support quality engineering for our Enterprise Data Platform. This role will focus on validating data pipelines and extending existing data quality frameworks within Databricks, ensuring data accuracy, quality, completeness, and reliability across the data lifecycle.
This role will play a key part in implementing, enhancing, and executing data validation and data quality checks across the medallion architecture.Key ResponsibilitiesExecute and extend existing automated tests within Databricks using PySpark, Python, SQL, and notebooks to validate data pipelinesPerform data reconciliation between source systems and target datasetsValidate ingestion processes including batch and incremental loadsTest transformations, joins, aggregations, and business rules for accuracyExtend and enhance existing data quality frameworks and rule setsImplement validation checks for data completeness, accuracy, consistency, and quality.Validate thresholds, alerts, and exception handling mechanismsSupport tracking of data quality metrics and trendsDevelop and maintain reusable and scalable test scripts aligned with existing frameworksIntegrate and execute tests within CI/CD pipelines (e.g., Azure DevOps)Support testing activities across environments (QA, Staging)Ensure consistent and reliable execution of automated testsPartner closely with Data Engineers and Data Quality Engineers to identify, troubleshoot, and resolve data issuesParticipate in Agile ceremonies and contribute to sprint deliverablesSupport defect triage, root cause analysis, and retestingEnsure data accuracy and consistency for downstream consumption and business reporting in data visualization tools such as Tableau.Required Skills & Expertise5+ years of experience in data validation, QA engineering, or SDET rolesHands-on experience with creating Databricks notebooks and data pipeline validationStrong proficiency in PySpark, Python, SQL, and Databricks notebooksExperience working with and extending existing data validation or data quality frameworksStrong experience in data reconciliation and large-scale data validationExperience executing tests within CI/CD pipelines (e.g., Azure DevOps)Strong analytical and problem-solving skillsExperience with tools such as Azure Purview and Profisee MDM is preferred.