Engineering Data Analyst

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).
• 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.

Skills & experience

Must-have

• 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).

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.

Tools & stack

• 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

Ways of working

• Clear written communication: problem statement, approach, assumptions, limitations, next steps.
• Stakeholder management for small projects: scoping, prioritization, and timeline expectations.
• Pragmatic approach to modeling: start simple, make it correct, then scale.

What You'll Get

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

€60,000 - €75,000 a year

We conduct background checks as part of our hiring process, in accordance with applicable laws and regulations in the countries where we operate. This may include verification of employment history, education, and, where legally permitted, criminal records. Any checks will be conducted lawfully prior to formal employment contracts being signed, with candidate consent, and information will be treated confidentially.

Pigment is an equal opportunity employer. We believe diversity is a strength and fosters innovation. We are committed to enabling everyone to feel included and valued at the workplace. All qualified applicants will receive consideration for employment without regard to age, color, family, gender identity, marital status, national origin, physical or mental disability, sex (including pregnancy), sexual orientation, social origin, or any other characteristic protected by applicable laws. We may process your personal data in accordance with our HR Data Protection Notice.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.