Description
Shape a brighter financial future with SoFi, a next-generation financial services company and national bank. The CTO Business Operations team enables SoFi's Engineering, Product, and Design (EPD) organization to execute with operational rigor, financial discipline, and organizational clarity.
As a Staff Data Analyst, you will own the data engineering, transforms, and reporting systems behind EPD headcount, organizational mapping, and org-wide resource reporting (~2,000+ people across Technology and Product).
Responsibilities:
- People Data Pipelines & Resource Reporting: Own and evolve internal org and squad resource reporting, driving reliable biweekly refreshes and high data integrity.
- Partner on the next evolution of resource reporting tooling, moving from manual spreadsheet round-trips toward automated Workday API ingestion and interactive drag-and-drop seat management for engineering leaders.
- Build and maintain automated people-data pipelines, Workday report transforms, and cross-system reconciliations across HRIS (Workday), Talent Acquisition, FP&A, and EPD rosters.
- Maintain accurate org mapping across divisions, groups, and squads, tracking hiring progress, backfills, contingent workers, team movements, and capacity impacts.
Data Engineering, Automation & AI-Enabled Analytics:
- Take messy, multi-source operational datasets and build clean, repeatable data models, scripts, and validation checks that eliminate manual spreadsheet toil.
- Use modern AI coding and analysis tools alongside SQL, Python (or equivalent scripting), and Google Sheets to rapidly prototype tooling, automate recurring reporting, and audit data quality.
- Ensure strong data governance, schema consistency, and clear documentation across CTO Business Operations datasets and tools.
Operating Rhythms, Resource Planning & Capitalization:
- Manage and consolidate EPD operating calendars, leadership forum rhythms, and recurring readiness milestones across Technology and Product.
- Support quarterly and annual headcount, contingent-worker, and resource planning cycles with clean baseline data, scenario models, and variance analysis.
- Support the quarterly software development capitalization and labor allocation process in partnership with Finance, Accounting, and engineering leaders.
- Perform ad-hoc analyses on organizational structure, squad composition, and workforce deployment to inform CTO leadership decisions.
Cross-Functional Partnership:
- Partner closely with CTO Business Operations, EPD leaders, People Partners, HRIS, Talent Acquisition, and Strategic Finance to keep staffing, org structure, and capacity data aligned.
- Translate complex engineering and workforce inputs into structured metrics, clear narratives, and actionable recommendations.
- Support executive-level summaries, dashboards, and operating materials for CTO leadership.
Requirements:
- 4 to 7+ years of experience in data engineering, analytics engineering, technical business operations, or data analysis.
- Strong data engineering foundations: proven ability to ingest, clean, transform, and reconcile messy, multi-source datasets using SQL, Python (or Apps Script / TypeScript), and advanced Google Sheets / Excel.
- Hands-on experience with Workday people data, custom reports, org hierarchies, and HRIS-to-planning reconciliations, with the ability to work toward API-driven data integrations.
- Fluency with modern AI coding and data tools (e.g., Cursor, Claude, Copilot, LLM workflows) to accelerate pipeline development, data cleaning, and tooling, paired with strong judgment on output accuracy.
- Track record of building and maintaining reliable internal tools, automated workflows, or operational dashboards for technical and executive stakeholders.
- High attention to detail, operational rigor, and ownership mindset: you spot reconciliation gaps before leaders do and drive them to zero.
- Strong written and verbal communication skills, with the ability to operate independently in a fast-paced, ambiguous environment.
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Finance, Economics, or equivalent practical technical experience.