Data Analyst (Data Quality) – Operations & Finance

You'll serve as the bridge between operational data (ticketing, sales, inventory, etc.) and financial reporting systems. Your job will be to ensure everything that happens in the business is accurately reflected in finance—through reconciliation models, alerting systems, and preventative data controls. Key Responsibilities Data Modeling & Reconciliation: Build and maintain data pipelines that link operational systems to financial/accounting systems (GL, ERP). Create reconciliation logic and define acceptable variances, automating alerts for anomalies. Automated Testing: Develop SQL or Python-based tests to calculate data variances, trigger alerts, and generate audit-ready evidence. Build Power BI dashboards for exception monitoring and variance explanations. Marketing Data Validation : Ensure UTM tags, pixels, and campaign data correctly feed into revenue KPIs. Work with the marketing team to maintain proper naming conventions and tagging consistency. Data Contracts & Governance: Define data contracts with owners (schema, nullability, thresholds) and implement validation checks. Participate in root cause analyses (RCA) and update SOPs/tests accordingly. Cross-Team Collaboration: Join forces with finance, operations, and IT to resolve issues, document flows and SOPs (in Confluence or similar), manage incidents (Jira/ServiceNow), and help business users adopt data quality best practices. Core Technical Skills Strong experience with SQL (e.g., Snowflake or similar) and Python for automation and reconciliation tasks. Proficiency with Power BI (DAX / Power Query) or other BI tools to create dynamic dashboards and financial reports. Familiarity with digital tracking (GA4, GTM, UTM tagging) and basic JavaScript debugging. Use of incident/ticketing tools like Jira or ServiceNow , and documentation in Confluence or similar. Bonus: experience with data quality tools (DBT, Talend, Alteryx), financial systems (SAP, Oracle), or data integration platforms (Fivetran, SSIS).