Azure Data Engineer: Marketing Spend KPIs & Pipelines

Grid Dynamics

Warszawa

On-site

PLN 150,000 - 210,000

Full time

7 days ago
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Job summary

Grid Dynamics is seeking a Data Engineer to join a newly launched business reporting initiative for a global food and beverage leader. You will bridge business stakeholders and a landscape of reporting tools, connecting multiple data sources, building ingestion and transformation pipelines on Azure, and consolidating advertising and marketing spend KPIs into a single source of truth.

The ideal candidate has commercial Azure Data Engineer experience, hands-on PySpark and SQL, and practical

Qualifications

  • Commercial experience as a Data Engineer on Azure.
  • Hands-on PySpark and SQL development.
  • Experience designing data pipelines with Azure Data Factory.
  • Understanding of Azure services and architecture.
  • Excellent communication with business stakeholders and adaptability to evolving requirements.
  • Nice to have: DevOpsCI/CD for Azure Data Factory or Databricks.

Responsibilities

  • Establish connections to existing reporting tools and data sources.
  • Design and build data pipelines in Azure Data Factory for ads data.
  • Process, clean and model data in Databricks using PySpark and SQL.
  • Consolidate KPIs into a central Master table as the single source of truth.
  • Collaborate with business stakeholders to clarify and adapt requirements.

Skills

PySpark
SQL
Azure Data Factory
Databricks
Data pipelines
Stakeholder communication

Tools

Azure Cloud Platform
Azure Data Factory
Databricks

Job description

Grid Dynamics is seeking a Data Engineer to join a newly launched business reporting initiative for a global food and beverage leader. You will bridge business stakeholders and a landscape of reporting tools, connecting multiple data sources, building ingestion and transformation pipelines on Azure, and consolidating advertising and marketing spend KPIs into a single source of truth.

The ideal candidate has commercial Azure Data Engineer experience, hands-on PySpark and SQL, and practical

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