Why This Job is Featured on The SaaS Jobs
This Engineering Data Analyst role sits at a mature, product-led B2B SaaS company where internal analytics is treated as part of how the product and engineering organisation operates. The remit spans R&D reporting, FinOps and AI usage impact, which reflects a SaaS environment where unit economics, reliability, and product investment decisions increasingly depend on well-defined metrics rather than one-off dashboards.
For a SaaS analytics career, the distinctive value is the proximity to how software gets built and shipped. Owning metric definitions, data contracts, and model documentation builds habits that transfer across modern SaaS data teams, especially as organisations push for self-serve analytics with governed datasets. Exposure to engineering metrics and cost analytics also develops an understanding of the operational levers behind gross margin and delivery throughput, not just customer or revenue reporting.
This role suits an analyst who prefers structured problem framing, careful data modeling, and written communication that makes reporting trustworthy for non-analytics stakeholders. It will appeal to someone comfortable balancing quick iterations with long-lived assets, and who wants their work to influence planning, staffing, and engineering execution in a SaaS setting.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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.
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Skills & experienceMust-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.
Nice-to-have
- Experience partnering closely with Engineering organizations (DevEx, reliability, platform, delivery metrics).
- 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 GetCompetitive 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
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€60,000 - €75,000 a year
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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.
