Engineering Data Analyst

Pigment; Paris; Sur place; EUR 60 000 - 75 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Equity

Comprehensive health insurance

Flexible working hours

Remote-friendly policy

Résumé du poste

Pigment is seeking a Data Analyst in Paris to deliver high-impact analyses and maintain R&D data models. You will be responsible for ensuring data reliability, implementing automated quality checks, and supporting leadership with accurate reporting.

This role requires 3–7+ years in data analytics, strong SQL skills, and a proven track record in data modeling. A competitive salary of €60,000 - €75,000 per annum is offered.

Qualifications

• Strong SQL skills required for reliable queries.
• Experience with data modeling concepts essential.
• Ability to analyze independently and communicate effectively.

Responsabilités

• Own data maintenance and reliability for R&D apps.
• Prepare leadership decision boards and recurring reports.
• Implement automated quality checks for trusted reporting.

Connaissances

SQL

Data Modeling

Analytical Skills

Communication

Formation

3–7+ years in Product Analytics / Data Analytics / BI

Outils

Snowflake

BigQuery

dbt

Looker

Description du poste

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.

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.

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 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.