Engineering Data Analyst

Mission

• Deliver high-impact analyses and data models that help R&D Engineering ship faster, operate reliably, and make better product decisions.
• Be a pragmatic analytics partner: iterate quickly, document clearly, and bias toward action.

What you’ll do

• Own the data maintenance and reliability of key R&D internal Pigment apps and reporting (FinOps, Engineering Metrics, AI usage/impact), including definitions and refresh cadence.
• Be accountable for R&D analytics models (documentation, maintenance, and evolution), from lightweight curated datasets to scalable handoff with central Data when needed.
• Define best practices for structuring and scaling R&D apps, including criteria for when to create a new app vs extend an existing one, and how to manage shared reference data.
• Implement automated quality checks and lightweight data contracts to ensure trusted reporting for leadership and teams.
• Enable self-serve by producing ready-to-use prompt templates and playbooks aligned to R&D’s most common questions.
• Prepare leadership decision boards and recurring reporting for staffing, reporting, and hiring discussions.
• Support ad hoc, small-scope initiatives (SaaS reviews, offsite preparation), R&D All Hands, and R&D process automation efforts (e.g., onboarding access, timesheets).
• Work in the Pigment app for internal purposes.

A typical first project would be to review and improve the R&D Reporting model (grain, definitions, consistency, and usability for stakeholders). Other needs involve insight collection about engineers’ work in connection to AI and the preparation of tested, curated boards for financial decision-making.

What success looks like

• Week 1–2: Understand R&D Engineering workflows, existing data sources, and current reporting gaps
• Month 1: Write an implementation proposal to re-model R&D analytics validated with modeling experts
• Months 2-3: Engineering teams and Leadership trust the R&D analytics model and leverage it for reporting systematically, thanks to prioritized coverage of R&D use cases, scheduled data routines, and automated checks

This is not exhaustive, as other smaller tasks may be overtaken in parallel, but delivering on this objective and timeline would be considered a full, successful deliverable.