Data Quality engineer

Job Description

Key Responsibilities Define and execute validation strategies for data pipelines, integrations, and transformations across Segment, SFMC, Snowflake, and downstream dashboards. Create and maintain automated data quality checks, test scripts, and regression suites to validate accuracy, completeness, and consistency of data. Support adoption of Trust the Data framework and ensure alignment with Medallion architecture (Bronze ? Silver ? Gold views). Validate logic used for user segmentation, lead qualification and such marketing activities Partner with Product Managers and Data Engineering teams to define acceptance criteria and test coverage for user stories in ADO/Jira. Work with product manager to ensure validated data supports marketing KPIs (campaign ROI, funnel conversion, lead quality, data accuracy %). Support onboarding of priority datasets into SODA DQ Scorecards and assist in interpreting data quality scores. Maintain test documentation, validation reports, and sign-offs for compliance and audit readiness. Required Skills Strong SQL skills for validating complex datasets in Snowflake (joins, aggregations, lineage checks). Hands-on experience with data validation frameworks (e.g., SODA). Familiarity with ETL/ELT workflows and validation of data pipelines. Exposure to marketing technology platforms (Segment, Salesforce Marketing Cloud, or similar CDPs/marketing automation tools). Ability to design comprehensive data test cases for completeness, accuracy, consistency, and timeliness. Strong problem-solving skills to identify root causes of data discrepancies and recommend remediation steps. Experience working with Medallion architecture (Bronze/Silver/Gold layers) and canonical data views. Strong written and verbal communication to translate technical data validation results into business-impact insights. Comfortable working in cross-functional teams with Product Managers, Data Engineers, Marketing stakeholders, and vendors. Experience with agile environments and tools like ADO, Jira, Confluence, Git. Preferred Experience 4?7 years of professional experience in data quality engineering, QA, or data engineering with a strong QA focus. Prior experience validating data flows across CDPs, CRM, or Marketing Automation platforms. Knowledge of AI/ML model validation for business applications Experience implementing data quality scorecards, lineage tracing, and validation dashboards. Familiarity with governance frameworks (e.g., GDPR/CCPA consent validation).