Data Engineer (ID

Amsterdam - Netherlands

Monthly Salary: Not Disclosed

Experience Required: 8-10years

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

As a Data Engineer you will:

Design build and maintain scalable data pipelines using Databricks PySpark and Azure Data Factory.
Develop and optimize data ingestion transformation and loading processes for structured and unstructured data sources.
Implement data governance frameworks to ensure proper data ownership compliance and security standards.
Build and maintain reliable data quality validation processes to ensure accuracy consistency and completeness of enterprise data.
Establish data lineage tracking mechanisms to provide transparency on how data flows across systems and pipelines.
Develop and maintain Python scripts and automation tools to streamline data engineering workflows.
Collaborate with data scientists analysts and business stakeholders to deliver high-quality datasets for analytics and AI initiatives.
Optimize data pipeline performance and scalability within Azure-based data platforms.

What You Bring to the Table:

8 years of experience in data engineering data platform development or big data environments.
Strong hands-on experience with Databricks and PySpark for distributed data processing.
Practical experience working with Azure Data Factory for orchestration and data pipeline development.
Advanced Python scripting skills for automation transformation and integration tasks.
Solid understanding of data governance principles including metadata management data cataloging and compliance.
Experience implementing data quality frameworks and validation rules within data pipelines.
Hands-on experience with data lineage tracking and documentation practices.
Experience working with large-scale data processing frameworks and cloud-based data platforms.

You should possess the ability to:

Architect and implement end-to-end data pipelines that support large-scale data processing.
Design efficient data models and transformation logic using PySpark and Databricks.
Implement data governance and compliance practices within enterprise data ecosystems.
Identify and resolve data quality issues proactively through automated validation and monitoring.
Track and document data lineage across multiple data sources and transformation layers.
Write clean scalable and maintainable Python code for data engineering workflows.
Collaborate effectively with cross-functional teams including data scientists analysts and platform engineers.
Troubleshoot and optimize data pipeline performance reliability and cost efficiency in Azure environments.

What We Bring to the Table:

Opportunity to work on large-scale cloud data platforms and advanced data engineering solutions.
Exposure to modern data stack technologies including Databricks Azure Data Factory and distributed processing frameworks.
Collaborative environment working with data scientists analytics teams and engineering professionals.
Challenging projects focused on building scalable and enterprise-grade data platforms.
Opportunities to enhance expertise in data governance data quality and lineage frameworks

Lets Connect

Want to discuss this opportunity in more detail Feel free to reach out.

Recruiter: Aswin Dhanvandhar

Phone: ; Extn :141

Email:

LinkedIn: Skills:

As a Data Engineer you will: Design build and maintain scalable data pipelines using Databricks PySpark and Azure Data Factory. Develop and optimize data ingestion transformation and loading processes for structured and unstructured data sources. Implement data governance frameworks to ensure proper data ownership compliance and security standards. Build and maintain reliable data quality validation processes to ensure accuracy consistency and completeness of enterprise data. Establish data lineage tracking mechanisms to provide transparency on how data flows across systems and pipelines. Develop and maintain Python scripts and automation tools to streamline data engineering workflows. Collaborate with data scientists analysts and business stakeholders to deliver high-quality datasets for analytics and AI initiatives. Optimize data pipeline performance and scalability within Azure-based data platforms. What You Bring to the Table: 8 years of experience in data engineering data platform development or big data environments. Strong hands-on experience with Databricks and PySpark for distributed data processing. Practical experience working with Azure Data Factory for orchestration and data pipeline development. Advanced Python scripting skills for automation transformation and integration tasks. Solid understanding of data governance principles including metadata management data cataloging and compliance. Experience implementing data quality frameworks and validation rules within data pipelines. Hands-on experience with data lineage tracking and documentation practices. Experience working with large-scale data processing frameworks and cloud-based data platforms. You should possess the ability to: Architect and implement end-to-end data pipelines that support large-scale data processing. Design efficient data models and transformation logic using PySpark and Databricks. Implement data governance and compliance practices within enterprise data ecosystems. Identify and resolve data quality issues proactively through automated validation and monitoring. Track and document data lineage across multiple data sources and transformation layers. Write clean scalable and maintainable Python code for data engineering workflows. Collaborate effectively with cross-functional teams including data scientists analysts and platform engineers. Troubleshoot and optimize data pipeline performance reliability and cost efficiency in Azure environments. What We Bring to the Table: Opportunity to work on large-scale cloud data platforms and advanced data engineering solutions. Exposure to modern data stack technologies including Databricks Azure Data Factory and distributed processing frameworks. Collaborative environment working with data scientists analytics teams and engineering professionals. Challenging projects focused on building scalable and enterprise-grade data platforms. Opportunities to enhance expertise in data governance data quality and lineage frameworks Lets Connect Want to discuss this opportunity in more detail Feel free to reach out. Recruiter: Aswin Dhanvandhar Phone: ; Extn :141 Email: LinkedIn:

As a Data Engineer you will:

Design build and maintain scalable data pipelines using Databricks PySpark and Azure Data Factory.
Develop and optimize data ingestion transformation and loading processes for structured and unstructured data sources.
Implement data governance frameworks to ensure proper data ownership compliance and security standards.
Build and maintain reliable data quality validation processes to ensure accuracy consistency and completeness of enterprise data.
Establish data lineage tracking mechanisms to provide transparency on how data flows across systems and pipelines.
Develop and maintain Python scripts and automation tools to streamline data engineering workflows.
Collaborate with data scientists analysts and business stakeholders to deliver high-quality datasets for analytics and AI initiatives.
Optimize data pipeline performance and scalability within Azure-based data platforms.

What You Bring to the Table:

8 years of experience in data engineering data platform development or big data environments.
Strong hands-on experience with Databricks and PySpark for distributed data processing.
Practical experience working with Azure Data Factory for orchestration and data pipeline development.
Advanced Python scripting skills for automation transformation and integration tasks.
Solid understanding of data governance principles including metadata management data cataloging and compliance.
Experience implementing data quality frameworks and validation rules within data pipelines.
Hands-on experience with data lineage tracking and documentation practices.
Experience working with large-scale data processing frameworks and cloud-based data platforms.

You should possess the ability to:

Architect and implement end-to-end data pipelines that support large-scale data processing.
Design efficient data models and transformation logic using PySpark and Databricks.
Implement data governance and compliance practices within enterprise data ecosystems.
Identify and resolve data quality issues proactively through automated validation and monitoring.
Track and document data lineage across multiple data sources and transformation layers.
Write clean scalable and maintainable Python code for data engineering workflows.
Collaborate effectively with cross-functional teams including data scientists analysts and platform engineers.
Troubleshoot and optimize data pipeline performance reliability and cost efficiency in Azure environments.

What We Bring to the Table:

Opportunity to work on large-scale cloud data platforms and advanced data engineering solutions.
Exposure to modern data stack technologies including Databricks Azure Data Factory and distributed processing frameworks.
Collaborative environment working with data scientists analytics teams and engineering professionals.
Challenging projects focused on building scalable and enterprise-grade data platforms.
Opportunities to enhance expertise in data governance data quality and lineage frameworks