# Engineering Data Analyst

**Company:** [Pigment](https://hotfix.jobs/companies/pigment)
**Location:** Paris, France
**Role:** Data Analytics
**Salary:** €60k – €75k/yr
**Experience:** 3+ years
**Skills:** SQL, Data Modeling, dbt, Snowflake, BigQuery, Looker, Mode, Tableau, Pigment, Git, Finops, Causal Inference, Observability, SLOs, Data Contracts
**Posted:** 2026-06-26

> The Engineering Data Analyst owns R&D analytics models, reporting reliability, data quality, and self-service insights. The role requires strong SQL, data modeling, stakeholder communication, and experience partnering with engineering teams.

## Job Description

## Responsibilities
- Own data maintenance and reliability for key R&D internal Pigment applications and reporting, including FinOps, engineering metrics, and AI usage/impact.
- Maintain and evolve R&D analytics models, from curated datasets to scalable handoff with central Data when needed.
- Define best practices for structuring and scaling R&D applications and managing shared reference data.
- Implement automated quality checks and lightweight data contracts for trusted reporting.
- Create prompt templates and playbooks to enable self-service analytics.
- Prepare leadership decision boards and recurring reporting for staffing, reporting, and hiring discussions.
- Support small-scope initiatives, R&D All Hands, and R&D process automation such as onboarding access and timesheets.
- Review and improve the R&D Reporting model, including grain, definitions, consistency, and stakeholder usability.
- Collect insights about engineers’ work in connection with AI and prepare tested, curated boards for financial decision-making.

## Requirements
- 3–7+ years, or equivalent experience, in Product Analytics, Data Analytics, or BI, ideally in a B2B SaaS environment.
- Strong SQL skills, including writing reliable, readable queries and building curated datasets.
- Experience with data modeling concepts such as facts and dimensions, grain, incremental builds, data contracts, and metric definitions.
- Ability to run analyses independently and communicate clearly with non-analytics audiences.
- Comfort working with ambiguous questions and iterating quickly.
- Experience partnering closely with engineering organizations, including DevEx, reliability, platform, or delivery metrics.
- Clear written communication covering problem statements, approaches, assumptions, limitations, and next steps.
- Stakeholder management skills for scoping, prioritization, and timeline expectations.
- A pragmatic approach to modeling: start simple, make it correct, then scale.

## Nice-to-haves
- Familiarity with dbt or a similar transformation layer and modern analytics stacks.
- Experience with experimentation and basic causal inference.
- Understanding of observability concepts, including logs, metrics, traces, SLOs, and incident analysis.
- Exposure to cost analytics or FinOps.

## Tools and Stack
- SQL and a data warehouse such as Snowflake or BigQuery
- dbt or a similar transformation layer
- BI tools such as Looker, Mode, Tableau, or Pigment
- Git for versioning models and documentation

## Compensation and Benefits
- Competitive salary
- Equity
- Comprehensive health insurance with Alan Blue for employees and their families
- Trust and flexible working hours
- Remote-friendly policy
- Offices in Paris, London, New York, and Toronto

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