Senior Azure Data Engineer (ID:3500)

Stafide

Amsterdam

On-site

EUR 70,000 - 90,000

Full time

14 days+

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Benefits offered by this job

Opportunities to work on large-scale projects
Collaborative environment
Learning-driven culture

Job summary

A leading technology company in Amsterdam is seeking a Senior Azure Data Engineer to design and implement scalable ETL/ELT data pipelines. You will utilize your extensive experience in Azure Data Factory, Databricks, and Python to optimize data workflows and ensure data quality. The ideal candidate has at least 6-8 years' experience in Data Engineering and is adept at translating business requirements into robust data engineering solutions. The role offers exposure to large-scale transformations within the Azure ecosystem.

Qualifications

  • 6–8 years of experience in Data Engineering or related roles.
  • Strong expertise in Azure Data Factory, Azure Databricks, and PySpark.
  • Proficiency in Python programming for data processing.
  • Advanced knowledge of SQL performance tuning.
  • Experience with Azure DevOps in data engineering.

Responsibilities

  • Design, develop, and maintain ETL/ELT data pipelines.
  • Build and optimize data transformation workflows.
  • Implement data quality frameworks and CI/CD pipelines.
  • Ensure adherence to security and compliance standards.

Skills

Azure Data Factory
Azure Databricks
Python
SQL performance tuning
Data governance
DevOps practices

Tools

Bicep

Job description

As a Senior Azure Data Engineer, you will:
  • Design, develop, and maintain scalable ETL/ELT data pipelines using Azure Data Factory (ADF).
  • Build and optimize data transformation workflows using Azure Databricks (PySpark) and Python.
  • Develop and fine-tune SQL queries, stored procedures, and views for high-volume data platforms.
  • Implement data quality frameworks, monitoring, and logging mechanisms across data pipelines.
  • Establish and manage CI/CD pipelines for data solutions using Azure DevOps.
  • Handle deployment automation for ADF pipelines, Databricks notebooks, and database scripts.
  • Manage version control, branching strategies, and environment configuration across development lifecycles.
  • Deploy and maintain Azure infrastructure components such as Azure SQL, Event Grid, Virtual Networks (VNet), Private Endpoints (PEP), Key Vaults, and related services using Bicep.
  • Ensure adherence to security, governance, and compliance standards across data platforms.
What You Bring to the Table:
  • 6–8 years of overall experience in Data Engineering or related roles.
  • Strong hands-on expertise in Azure Data Factory, Azure Databricks, and PySpark.
  • Proficiency in Python programming for data processing and automation.
  • Advanced knowledge of SQL performance tuning and data modeling.
  • Experience implementing DevOps practices in data engineering environments using Azure DevOps.
  • Solid understanding of Azure cloud infrastructure and Infrastructure as Code using Bicep.
  • Experience working with enterprise-scale data ingestion, transformation, and orchestration.
  • Familiarity with data governance, security best practices, and compliance frameworks.
You Should Possess the Ability To:
  • Design and implement high-performance, scalable, and reliable data pipelines.
  • Troubleshoot, optimize, and enhance data workflows for performance and cost efficiency.
  • Collaborate with cross-functional teams including analytics, application development, and infrastructure teams.
  • Automate deployments and enforce best practices for code versioning and release management.
  • Translate business and technical requirements into robust data engineering solutions.
  • Ensure strong documentation, monitoring, and operational support for data platforms.
What We Bring to the Table:
  • Opportunities to work on large-scale, cloud-based data transformation initiatives.
  • Exposure to modern Azure data and analytics ecosystem.
  • A collaborative environment focused on innovation, automation, and continuous improvement.
  • Opportunities to contribute to architecture design and next-generation data platform development.
  • A learning-driven culture encouraging adoption of emerging cloud and data engineering technologies.
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