The Role
Design, build, and maintain scalable Azure data pipelines and ETL workflows using Databricks, Data Factory, PySpark, Python and Airflow. Manage run/ops, monitor performance, resolve incidents, support data governance with Atlas, and create technical documentation and runbooks while collaborating across teams to ensure reliable enterprise data delivery.
Summary Generated by Built In
As a Senior Azure Data Engineer, you will:
- Design, develop, and maintain scalable data pipelines and data processing solutions using Azure Databricks and Azure Data Factory.
- Build, optimize, and support ETL workflows to ensure reliable and timely data ingestion and transformation across enterprise data platforms.
- Manage run and operational activities, including monitoring data pipelines, identifying issues, and resolving incidents within defined SLAs.
- Ensure timely and accurate data onboarding from source systems into enterprise data environments.
- Develop and enhance data processing logic using Python, PySpark, and SQL to support large-scale analytics and reporting needs.
- Implement and manage workflow orchestration and scheduling using Apache Airflow.
- Support data governance and metadata management initiatives using the Atlas Framework.
- Troubleshoot and resolve complex data pipeline, performance, and data quality issues in production environments.
- Collaborate with cross-functional teams to ensure data availability, reliability, and operational stability.
- Create and maintain technical documentation, operational procedures, and best practices for data engineering processes.
What You Bring to the Table:
- 6–8 years of overall experience in data engineering and enterprise data platform environments, with a strong focus on cloud-based data solutions.
- Proven hands-on experience in designing, developing, and maintaining data pipelines using Azure Databricks.
- Strong practical experience in building and managing data integration workflows using Azure Data Factory.
- Advanced proficiency in Python for data processing, automation, and pipeline development.
- Solid hands-on experience with PySpark for large-scale distributed data processing.
- Strong command of SQL for data querying, transformation, and performance optimization.
- Demonstrated experience in designing and supporting ETL pipelines in production environments.
- Practical experience using Apache Airflow for workflow orchestration and scheduling.
- Working knowledge of the Atlas Framework for data governance and metadata management.
- Experience supporting data platforms in run and operations mode, including incident management and SLA adherence.
- Strong analytical, troubleshooting, and problem-solving skills.
- Effective communication skills and the ability to collaborate with cross-functional technical teams.
You Should Possess the Ability to:
- Design and implement scalable, reliable, and high-performance data engineering solutions on Azure.
- Automate and optimize data processing workflows using Python and PySpark.
- Proactively identify, analyze, and resolve data pipeline and performance issues.
- Manage operational responsibilities while ensuring data accuracy and timely data delivery.
- Work independently while taking ownership of end-to-end data engineering tasks.
- Collaborate effectively with technical and non-technical stakeholders.
- Develop and maintain clear technical documentation and operational runbooks.
What We Bring to the Table:
- Opportunities to work on enterprise-scale Azure data engineering initiatives.
- Exposure to modern cloud-based data platforms and advanced data engineering technologies.
- A collaborative and professional environment focused on operational excellence and data reliability.
- Hands-on experience with complex data ecosystems and enterprise-level platforms.
- Continuous learning and professional growth opportunities in cloud data engineering.
Let’s Connect
Want to discuss this opportunity in more detail? Feel free to reach out.
Recruiter: Asha Krishnan
Phone: +31 20 369 0609 ; Extn :146
LinkedIn: https://www.linkedin.com/in/asha-krishnan
Skills Required
- 6-8 years of experience in data engineering and enterprise data platforms
- Hands-on experience designing, developing, and maintaining data pipelines using Azure Databricks
- Experience building and managing data integration workflows using Azure Data Factory
- Advanced proficiency in Python for data processing and automation
- Solid hands-on experience with PySpark for large-scale distributed data processing
- Strong command of SQL for querying, transformation, and performance optimization
- Experience designing and supporting ETL pipelines in production environments
- Practical experience using Apache Airflow for workflow orchestration and scheduling
- Working knowledge of the Atlas Framework for data governance and metadata management
- Experience supporting data platforms in run and operations mode, including incident management and SLA adherence
- Strong analytical, troubleshooting, problem-solving, and communication skills
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The Company
What We Do
STAFIDE is a Netherlands-based niche technology talent consulting company operating across Europe. It helps organizations identify, recruit, and deploy technology professionals in areas including cybersecurity, cloud engineering, software development, data analytics, ERP, infrastructure, and digital transformation. Its services include recruitment, workforce engagement, secondment, onboarding support, workforce deployment, and workforce analytics that support technology hiring and expansion.
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