Engineering Data Analyst

Pigment is the AI-powered business planning and performance management platform built for agility and scale. We connect people, data, and processes in one intuitive, feature-rich solution, empowering every team—from Finance to HR—to build, adapt, and align strategic plans in real time. Founded in 2019, Pigment is one of the fastest-growing SaaS companies globally. Industry leaders like Unilever, Snowflake, Siemens, and DPD use Pigment daily to make more informed decisions and confidently navigate any scenario. With a team of 600+ across Paris, London, New York, Toronto, San Francisco and Austin, we've raised nearly $400M from top-tier investors and were named a Visionary in the 2024 Gartner® Magic Quadrant™ for Financial Planning Software. 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.

Responsibilities

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; 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

Requirements

3–7+ years (or equivalent) in Product Analytics / Data Analytics / BI, ideally in a B2B SaaS environment; Strong SQL: ability to write reliable, readable queries and build curated datasets; Proven experience with data modeling concepts (facts/dimensions, grain, incremental builds, data contracts, metric definitions); Ability to run analyses independently and communicate clearly to non-analytics audiences; Comfort working with ambiguous questions and iterating quickly; Experience partnering closely with Engineering organizations (DevEx, reliability, platform, delivery metrics); SQL + data warehouse (e.g., Snowflake/BigQuery); dbt or similar transformation layer; BI tool (e.g., Looker/Mode/Tableau/Pigment); Git for versioning of models and documentation

Nice to have

Familiarity with dbt (or similar) and modern analytics stacks; Experience with experimentation and causal inference basics; Understanding of observability concepts (logs/metrics/traces), SLOs, incident analysis; Exposure to cost analytics / FinOps

Benefits

Competitive salary; Equity; Comprehensive health insurance with Alan Blue (free for you and your family); Trust and flexible working hours; Remote-friendly policy; Brand new offices in Paris, London, New York, and Toronto