Analytics Engineer: Build Trusted Analytics Data Products

team.blue

Italia

On-site

EUR 70,000 - 110,000

Full time

47 hours ago
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Job summary

team.blue is seeking an experienced Analytics Engineer to join the AI & Data function. You will transform raw data into analytics-ready datasets, build KPIs and semantic models, and deliver data products across marketing, product usage, customer care, revenue, and more.

You will collaborate with data engineers, analytics engineers, and business stakeholders to enable self-service reporting and dashboards. You will partner with cross-brand teams, apply best practices in data governance, and

Qualifications

  • 5+ years in analytics engineering, data engineering, or related data role.
  • Proficiency with dbt Core/Cloud is required.
  • Experience on Databricks or similar cloud platforms.

Responsibilities

  • Design and build dbt models on Databricks that produce clean, conformed datasets.
  • Define KPI logic in collaboration with stakeholders and ensure consistent definitions.
  • Maintain semantic/presentation layer and ensure data products are reliable, tested, documented, and performant.
  • Apply software engineering practices: version control, testing, CI/CD, and documentation.
  • Onboard new data domains autonomously, exploring data and validating context with domain owners.
  • Identify data quality issues early and drive resolution with Data Management.

Skills

dbt
Databricks
Dimensional modeling
SQL
Autonomy
Stakeholder communication
Cross-domain data

Tools

Looker
Tableau
Power BI
Python

Job description

team.blue is seeking an experienced Analytics Engineer to join the AI & Data function. You will transform raw data into analytics-ready datasets, build KPIs and semantic models, and deliver data products across marketing, product usage, customer care, revenue, and more.

You will collaborate with data engineers, analytics engineers, and business stakeholders to enable self-service reporting and dashboards. You will partner with cross-brand teams, apply best practices in data governance, and

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