Engineering Data Analyst

Pigment

Paris, FR

On-siteCareer-pivot friendly

Who this role is best for

Best suited to mid-level data analysts with experience in R&D engineering workflows and pragmatic analytics, working in Paris.

Best fit for

Analysts who thrive on iterating quickly and documenting clearly for engineering teams.
— “Be a pragmatic analytics partner: iterate quickly, document clearly”
Candidates experienced in maintaining and evolving data models for R&D.
— “Be accountable for R&D analytics models (documentation, maintenance, and evolution)”
Professionals comfortable with automating quality checks for trusted reporting.
— “Implement automated quality checks and lightweight data contracts”

Things to consider

Initial focus on reviewing and improving R&D Reporting model within weeks.
— “A typical first project would be to review and improve the R&D Reporting model”
Expect to support ad hoc initiatives like R&D All Hands and process automation.
— “Support ad hoc, small-scope initiatives (SaaS reviews, offsite preparation)”

How to stand out

Demonstrate prior success in enabling self-serve analytics with ready-to-use templates.
— “Enable self-serve by producing ready-to-use prompt templates”
Highlight experience in preparing leadership decision boards for staffing and hiring.
— “Prepare leadership decision boards and recurring reporting for staffing”
Showcase examples of defining best practices for structuring and scaling apps.
— “Define best practices for structuring and scaling R&D apps”

Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · IndividualLevel · Mid Level

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

improved data maintenance and reliability
defined best practices for R&D apps
implemented automated quality checks

Typical background

data analystdata engineer

Skills & requirements

Required

Data AnalysisData ModelingSQLETL ProcessesData Visualization

Preferred

AI ToolsPython

Stack & domain

Data AnalysisData ModelingData MaintenanceData ReliabilityData ContractsPrompt TemplatesPlaybooksReportingAIPragmaticIterativeDocumentingBias Toward ActionBusiness PlanningPerformance ManagementFinanceHrEngineering

About the role

Original posting from Pigment via Lever

Join Pigment: The AI Platform Redefining Business Planning
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.
At Pigment, we take smart risks, celebrate bold ideas, and challenge the status quo—all while working as one team. If you're driven by innovation and ready to make an impact at scale, we’d love to hear from you.
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).
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.

Source: Pigment careers (Lever)