Hands-on Data Engineering Lead (Azure + Airflow) for BI

emagine

Warszawa

Hybrid

PLN 210,000 - 260,000

Full time

13 hours ago
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Benefits offered by this job

Hybrid work arrangement

Job summary

emagine is seeking an experienced Senior Data Engineer / Data Engineering Lead to own the Data Engineering domain within a BI-focused team. This hands-on role requires taking technical ownership, guiding other developers and improving data pipelines, models and reporting foundations.

The position is hybrid (2 days in the Warsaw office; rest remote), with onboarding to the current data landscape and architecture.

Qualifications

  • Proven senior data engineering experience in a BI-focused team.
  • Strong SQL and data modelling skills with hands-on Python work.
  • Experience with Azure Data Factory or Apache Airflow and Azure data services.

Responsibilities

  • Own the Data Engineering area within a BI-focused team.
  • Design, build and optimize data pipelines and transformations.
  • Create analytical data models and complex SQL transformations.
  • Develop pipelines using Python, Azure Data Factory and/or Airflow.
  • Identify gaps, bottlenecks and improvements; troubleshoot end-to-end data flows.
  • Provide technical guidance to BI Developers and team members.
  • Support engineering standards, design decisions and best practices.
  • Collaborate with Power BI Developers, Architects and stakeholders.
  • Ensure data layer reliability and scalable foundations for reporting.

Skills

Advanced SQL
Data modelling
Python
Power BI knowledge
Troubleshooting
Independent ownership
Communication
Technical leadership

Tools

Azure Data Factory
Apache Airflow

Job description

emagine is seeking an experienced Senior Data Engineer / Data Engineering Lead to own the Data Engineering domain within a BI-focused team. This hands-on role requires taking technical ownership, guiding other developers and improving data pipelines, models and reporting foundations.

The position is hybrid (2 days in the Warsaw office; rest remote), with onboarding to the current data landscape and architecture.

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