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data engineer for sustainability data

Summary

Data engineer building and maintaining production data pipelines and services for GRESB's ESG/sustainability benchmarks, using Python, SQL, and cloud infrastructure with IaC and containers.

Описание

GRESB provides financial markets with actionable sustainability insights, data, and benchmarks for real assets and climate-critical industries. Investors and managers use its standardized, validated ESG data to assess risk, improve portfolio quality, and guide long-term investment decisions.

Задачи

  • Build and maintain data pipelines and services supporting evolving annual standards and methodology, and re-architect or decommission unsuitable ones
  • Shape data models, schemas, and serving layers for reporting and analytics
  • Own data quality and reliability through validation, automated testing, and production monitoring
  • Make data discoverable and documented through definitions, lineage, and pipeline logic
  • Guide architecture decisions by framing options and trade-offs and bringing them to team decisions
  • Raise engineering standards through design and code review
  • Mentor others through complex work and turn learnings into reusable team patterns

Требования

  • Proven experience building and running production data pipelines and services end-to-end
  • Strong Python and SQL skills
  • Sufficient data analysis skills to interrogate datasets and answer questions
  • Hands-on experience running cloud systems with infrastructure as code, containers, and continuous deployment
  • Solid knowledge of data modelling and warehouse design

Условия

  • Competitive salary matching skills and experience
  • holiday allowance paid monthly
  • 25 Days of annual leave and 2 volunteering days off per year
  • Company pension plan
  • Free public transport to and from the Amsterdam office
  • Free daily catered office lunch
  • Annual professional development budget and one-time home office budget
  • Annual hackathon and opportunities to own projects and change working practices
  • Company-wide AI tooling

See also

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