Senior azure databricks engineer(id: 4002)

Stafide

Amstelveen, Netherlands

1 day ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

clk.platform.jobster.com

Tech stack

Agile Methodology Microsoft Azure Continuous Integration Data Architecture Information Engineering Data Transformation Data Systems Python (Programming Language) Scaled Agile Framework SQL Databases Data Streaming YAML

+9 more

Data Processing Reliability of Systems Infrastructure as Code (IaC) Pyspark Infrastructure Automation Frameworks Deployment Automation Bicep Data Pipelines Databricks

Job description

Design, develop, and maintain scalable and reliable data processing solutions using Azure Databricks.
Build and manage robust batch and streaming data pipelines within Databricks environments.
Develop and optimize data transformation and processing solutions using Python, PySpark, and SQL.
Design and optimize data models to support scalable processing, performance, and reliability.
Manage multiple parallel data processing workflows and shared data sources efficiently.
Implement and maintain CI/CD pipelines using Azure DevOps and YAML-based configurations.
Apply Infrastructure as Code (IaC) using ARM/Bicep for deployment and infrastructure automation.
Monitor, troubleshoot, and optimize data processing workloads and Databricks environments.
Collaborate with cross-functional engineering and business teams to deliver reliable and maintainable data solutions.
Contribute to Agile development practices and continuously improve engineering standards, system stability, and performance., * Build scalable, high-performance, and reliable data processing solutions using Azure Databricks.
Develop efficient PySpark and SQL-based data transformations.
Design and manage complex batch and streaming workloads.
Optimize data pipelines, processing performance, and resource utilization.
Troubleshoot complex data engineering issues and improve system reliability.
Implement automated deployment and infrastructure management practices.
Make pragmatic technical decisions while maintaining scalability and maintainability.
Work effectively with engineering, architecture, and business stakeholders.
Drive continuous improvement and maintain high standards of code and solution quality.

Requirements

Strong hands-on experience with Azure Databricks as a core data engineering platform.
Strong proficiency in Python, PySpark, and SQL.
Hands-on experience developing batch and streaming data pipelines.
Experience with data modeling, transformation, and optimization within Databricks environments.
Good understanding of Azure cloud services relevant to data engineering.
Experience with Azure DevOps, CI/CD, and YAML-based pipeline configurations.
Hands-on experience with Infrastructure as Code, particularly ARM/Bicep.
Experience working in Agile engineering and delivery environments.
Understanding of modern cloud-based data architectures and end-to-end data engineering solutions.
Strong communication, collaboration, troubleshooting, and problem-solving skills.

Benefits & conditions

Opportunity to work on enterprise-scale Azure and Databricks data engineering initiatives.
Exposure to modern cloud-based data platforms and engineering practices.
A collaborative Agile environment focused on technical excellence and innovation.
Opportunities to work with advanced data processing, pipeline engineering, and cloud technologies.
Continuous learning and opportunities for technical and professional growth.
A culture focused on quality, ownership, scalability, and sustainable engineering solutions.