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Data & Analytics Engineer (Python / dbt / GCP)

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Summary

Builds and maintains analytical data models, data pipelines, and integrations from existing platforms (including Informatica), while supporting Python-based ML/AI initiatives. Core stack: Python, SQL, dbt, and Google Cloud Platform (GCP), with Looker/BigQuery exposure.

About the Role

We are looking for a versatile Data & Analytics Engineer to support the development of our data and analytics capabilities. The role combines analytics engineering, data integration and Python-based AI/ML development.

The successful candidate will be responsible for building and maintaining analytical data models, developing data pipelines and integrating data from existing platforms, including Informatica. The role will also involve supporting machine learning and AI engineering initiatives, preferably within the Google Cloud Platform (GCP) ecosystem.

What We Offer

  • The opportunity to work across analytics engineering, data integration and AI/ML.
  • Exposure to modern data technologies and cloud-based solutions.
  • A collaborative environment with opportunities to contribute to the evolution of the data platform.
  • Proven experience in data engineering, analytics engineering or a similar role.
  • Strong Python and SQL skills.
  • Hands-on experience with dbt and data transformation.
  • Experience with data modeling and semantic layer concepts.
  • Familiarity with Google Looker or similar business intelligence and analytics tools.
  • Experience with data integration, extraction and processing from multiple sources.
  • Knowledge of Informatica or experience working with data originating from Informatica-based platforms.
  • Understanding of machine learning and AI engineering concepts, with practical experience developing Python-based solutions.
  • Familiarity with cloud-based data platforms, preferably Google Cloud Platform (GCP).
  • Ability to work independently across different areas of the data lifecycle.

Nice to Have

  • Experience with BigQuery and Google Cloud data services.
  • Experience developing and deploying ML/AI solutions in a cloud environment.
  • Familiarity with data orchestration tools and modern data stack architectures.
  • Experience building reusable semantic models and standardized business metrics.

Skills

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See also

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