Engineering Data Analyst
On-siteLondon, England, United Kingdom or New York City, New York, United States
Job Summary
Engineering Data Analyst at Pigment — deliver high-impact analyses and data models to help R&D Engineering ship faster, operate reliably, and inform product decisions. Own the data maintenance and reliability of key R&D internal Pigment apps and reporting (FinOps, Engineering Metrics, AI usage/impact), define data structures and data contracts, implement automated quality checks, and enable self-serve by producing ready-to-use prompt templates and playbooks. Prepare leadership decision boards and recurring reporting for staffing, reporting, and hiring discussions; support ad hoc initiatives and process automation. Skills include strong SQL, data modeling, and ability to collaborate with engineering teams and non-analytics audiences, with experience in modern analytics stacks and, ideally, dbt, Looker/Mode/Tableau, and data warehouse platforms. Benefits include competitive salary, equity, health insurance, flexible hours, remote-friendly policy, and offices in Paris, London, New York, and Toronto.
Required Qualifications
- 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
Desired Qualifications
- 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
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