Senior Data Engineer (Azure/Databricks)
New
C
C&FData analytics
Workplace type: remoteFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Very good knowledge of English
- Experience
- 4+ years of experience in Azure and 6+ years of industrial experience in large-scale data management, visualization, and analytics
- Required Skills
- PythonSQLMicrosoft AzureCI/CDAzure DevOps
Requirements
- Have 4+ years of experience in Azure.
- Have 6+ years of industrial experience in large-scale data management, visualization, and analytics.
- Bring strong hands-on experience with Azure Databricks, including data engineering, data processing, and analytics workloads.
- Have hands-on knowledge of Azure Data Lake, Azure SQL, and Azure Data Factory.
- Have good knowledge of SQL and Python.
- Have very good knowledge of English and be able to explain technical topics clearly to business stakeholders.
- Bring a proactive, goal-oriented approach and strong problem-solving skills.
- Be willing to attend the Warsaw office as needed for customer visits, workshops, or important project periods requiring face-to-face teamwork.
- Nice to have: experience designing and implementing CI/CD pipelines for data and analytics solutions, preferably with Azure DevOps.
- Nice to have: experience with Databricks Workflows and knowledge of DevOps practices, version control, automated testing, and deployment processes.
Responsibilities
- Design, develop, and maintain scalable data solutions in Azure Databricks for analytics and BI.
- Manage the full data lifecycle, from acquisition and integration through analysis and visualization.
- Build and maintain CI/CD pipelines using Azure DevOps and/or GitHub, automating testing, deployment, and operations.
- Deliver end-to-end data and analytics projects, from requirements and architecture through implementation and deployment.
- Develop and optimize data pipelines, visualizations, analytics products, automated services, and APIs.
- Ingest and integrate data from diverse sources into scalable, maintainable data platforms.
- Ensure the performance, reliability, scalability, and data quality of data solutions.
- Collaborate with multidisciplinary teams to develop models, insights, and production-ready solutions.
- Advise clients and stakeholders on data, Databricks, and solution architecture, and identify optimization opportunities.
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