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Handshake is seeking a Data Analyst to support the Finance and Central Operations organization. The role combines analytics, data engineering, and applied data science with a focus on translating complex operational data into reliable financial insights.
Key responsibilities include building and maintaining scalable Finance and Operations dashboards across revenue, project expenses, margins, cash flows, and payments. You will develop automated data pipelines that integrate information from financial, operational, and business systems, identifying and executing opportunities to automate existing financial reporting processes and reduce manual spreadsheet-based work.
A significant portion of the role involves partnering with the Payment Operations team to build reporting and analytics around payment volumes, accuracy, exceptions, disputes, and processing performance. You will develop automated reconciliations and controls to identify missing, duplicate, incorrect, or unusual payments, and support investigation and resolution of payment discrepancies through data analysis.
You will help improve and automate payment workflows, including data validation and exception management, while partnering with Finance and Operations stakeholders to translate business questions into data models, dashboards, and actionable analysis. Establishing documentation, data definitions, and controls that improve the reliability and auditability of Finance and Payments reporting is also critical.
Required experience includes 3–6+ years in data analytics, analytics engineering, data engineering, financial analytics, or related fields, with proficiency in Python or another analytical programming language. Advanced SQL skills, experience with modern data warehouses (Snowflake, BigQuery, Databricks), and dashboard tools (Hex, Looker, Tableau, Power BI) are highly valued. Experience with Finance teams and payment operations is strongly preferred.
The role requires close collaboration with the San Francisco-based team and availability during core U.S. business hours.
