Engineering Data Analyst

Responsibilities

• ▹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, evolution), from lightweight curated datasets to scalable handoff with the central Data team
• ▹Define best practices for structuring and scaling R&D apps, including 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
• ▹Enable self-serve by producing ready-to-use prompt templates and playbooks for 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 process automation (onboarding access, timesheets)
• ▹Work within the Pigment app for internal purposes; a first project would be reviewing and improving the R&D Reporting model (grain, definitions, consistency, usability)

Soft skills

About the company

Pigment is the AI-powered business planning and performance management platform built for agility and scale. Founded in 2019, it is one of the fastest-growing SaaS companies globally, used daily by Unilever, Snowflake, Siemens and DPD. Its 600+ person team spans Paris, London, New York, Toronto, San Francisco and Austin; the company has raised nearly $400M and was named a Visionary in the 2024 Gartner Magic Quadrant for Financial Planning Software.