Senior Data Engineer Azure (ID: 4054)

Stafide

Warszawa

On-site

PLN 180,000 - 240,000

Full time

14 days+

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Job summary

Stafide is seeking a Senior Data Engineer - Azure to design, develop, and maintain scalable data pipelines and enterprise data products across cloud and on-prem environments.

You will implement ETL/ELT with Azure Data Factory, Synapse, ADLS, and Databricks, build CI/CD pipelines, and work in Agile teams with stakeholders. Strong SQL and Python, 6+ years experience, and excellent communication are required.

Qualifications

  • 6+ years of professional experience in Data Engineering, Data Warehousing, or related field.
  • Hands-on experience with Azure Data Factory (ADF).
  • Strong experience with Azure Synapse Analytics.
  • Strong experience with Azure Data Lake Storage (ADLS).
  • Strong hands-on experience with Azure Databricks.
  • Strong experience with SQL Server, T-SQL, and SSIS.
  • Hands-on experience with CI/CD, Azure DevOps, and GitHub.
  • Strong proficiency in SQL and Python.
  • Good knowledge of data warehousing, dimensional modelling, and ETL/ELT concepts.
  • Experience delivering production-ready data solutions in an Agile environment.
  • Strong knowledge of Git-based development practices, including branching, pull requests, code reviews, and release management.

Responsibilities

  • Design, develop, and maintain scalable data pipelines and enterprise data products.
  • Build and support data engineering solutions across cloud and on-premises environments.
  • Design and implement robust ETL/ELT processes for enterprise data solutions.
  • Develop and maintain CI/CD pipelines using Azure DevOps and GitHub.
  • Apply Git-based development practices including branching, pull requests, code reviews, and release management.
  • Optimize data pipelines, ETL/ELT processes, and data models for performance and data quality.
  • Troubleshoot and resolve production data engineering and pipeline issues.
  • Collaborate with business and technical stakeholders to understand requirements and deliver data solutions.
  • Maintain clear technical documentation for data solutions and processes.
  • Contribute to Agile delivery and continuous improvement of production-ready data solutions.

Skills

SQL
Python
Git
CI/CD
Azure DevOps
Communication
Stakeholder management
Analytical thinking
Agile
Troubleshooting

Tools

Azure Data Factory
Azure Synapse Analytics
Azure Data Lake Storage
Azure Databricks
SSIS
SQL Server

Job description

As a Senior Data Engineer - Azure, you will:
  • Design, develop, and maintain scalable and reliable data pipelines and enterprise data products.
  • Build and support data engineering solutions across cloud and on-premises environments.
  • Design and implement robust ETL/ELT processes for enterprise data solutions.
  • Develop and maintain CI/CD pipelines using Azure DevOps and GitHub.
  • Apply Git-based development practices including branching, pull requests, code reviews, and release management.
  • Optimize data pipelines, ETL/ELT processes, and data models for performance, stability, scalability, and data quality.
  • Troubleshoot and resolve production data engineering and pipeline issues.
  • Collaborate with business and technical stakeholders to understand requirements and deliver effective data solutions.
  • Maintain clear and accurate technical documentation for data solutions and processes.
  • Contribute to the delivery and continuous improvement of production-ready data solutions within an Agile environment.
What You Bring to the Table:
  • 6+ years of overall professional experience in Data Engineering, Data Warehousing, or a related field.
  • Strong hands-on experience with Azure Data Factory (ADF).
  • Strong experience with Azure Synapse Analytics.
  • Strong experience with Azure Data Lake Storage (ADLS).
  • Strong hands-on experience with Azure Databricks.
  • Strong experience with SQL Server, T-SQL, and SQL Server Integration Services (SSIS).
  • Hands-on experience with CI/CD, Azure DevOps, and GitHub.
  • Strong proficiency in SQL and Python.
  • Good knowledge of data warehousing, dimensional modelling, and ETL/ELT concepts.
  • Experience delivering production-ready data solutions in an Agile environment.
  • Strong knowledge of Git-based development practices, including branching, pull requests, code reviews, and release management.
  • Strong analytical and troubleshooting skills.
  • Strong communication and stakeholder-management skills.
You should possess the ability to:
  • Design and develop scalable, reliable, and maintainable data pipelines.
  • Build enterprise data solutions using the Azure data engineering ecosystem.
  • Develop and optimize ETL/ELT workflows using Azure Data Factory, Databricks, and SSIS.
  • Work effectively with Azure Synapse, ADLS, SQL Server, and T-SQL.
  • Develop efficient and high-quality SQL and Python solutions.
  • Design and work with data warehouses and dimensional data models.
  • Optimize data pipelines and data models for performance, stability, scalability, and data quality.
  • Build and manage CI/CD pipelines using Azure DevOps and GitHub.
  • Follow Git-based development practices and participate effectively in code reviews and release management.
  • Troubleshoot complex production issues and identify appropriate root causes and solutions.
  • Work effectively across cloud and on-premises data environments.
  • Collaborate with business and technical stakeholders and translate requirements into technical solutions.
  • Document technical solutions, processes, and implementation details clearly.
  • Work effectively within Agile teams and take ownership of assigned deliverables.
What we bring to the table:
  • The opportunity to work on scalable and enterprise-grade Azure data engineering solutions.
  • Exposure to a broad Azure data technology stack including ADF, Synapse, ADLS, and Databricks.
  • Opportunities to work extensively with SQL Server, T-SQL, SSIS, SQL, and Python.
  • Hands-on experience with modern CI/CD, Azure DevOps, GitHub, and Git-based development practices.
  • Opportunities to work across both cloud and on-premises data environments.
  • Exposure to enterprise data warehousing, dimensional modelling, ETL/ELT, and data product development.
  • A collaborative Agile environment involving business and technical stakeholders.
  • Opportunities to contribute to performance optimization, data quality, production support, and continuous improvement.
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