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GoFundMe is seeking a Staff Data Engineer to build scalable data infrastructure and automation for the Accounting and Finance function. This role sits at the intersection of data engineering, accounting automation, financial reporting, and compliance—working with Analytics, Accounting, Finance, Payments, and Engineering teams to ensure financial data is complete, accurate, traceable, and audit-ready.
Key responsibilities include:
• Own the data pipelines, transformations, and technical systems powering Accounting and Finance reporting. Ensure financial data flows reliably from source systems through downstream reporting and reconciliation workflows.
• Partner with the Analytics team to build the engineering foundation beneath financial reporting and analysis. Translate analytical and business requirements into durable data products while enabling analysts to own reporting and insights.
• Integrate AI-assisted tools and coding agents into daily workflows to accelerate prototyping, implementation, testing, debugging, and documentation without compromising accuracy, security, or maintainability.
• Enable compliance-driven platform requirements by building data capabilities for regulatory and compliance reporting. Design compliance infrastructure to be automated, repeatable, and audit-ready from inception, leveraging scalable pipelines so regulatory obligations are met through durable systems rather than manual processes.
• Develop data infrastructure that makes financial reporting reproducible, traceable, and defensible, with appropriate lineage, documentation, controls, and evidence to support audits and enterprise-scale reporting.
• Build and maintain reconciliation systems across payment processors, internal transaction systems, the ledger, and financial platforms like NetSuite. Develop controls and monitoring to surface missing, duplicated, delayed, or inconsistent transactions.
• Monitor data integrity across financial systems moving between source systems, payment platforms, the transaction ledger, Snowflake data warehouse, and NetSuite. Use automation and AI-assisted investigation to identify patterns and accelerate root-cause analysis.
• Define best practices and engineering standards for how financial data pipelines and models are designed, tested, documented, monitored, and maintained. Build resilient, observable systems understandable beyond original authors.
• Implement automated testing, alerting, lineage, and monitoring for critical financial datasets. Expand test coverage, identify anomalies, and shorten detection and resolution times for data quality issues.
• Operate independently across functions, translating complex business and regulatory requirements into clear technical plans and communicating progress to diverse stakeholders.
