Senior Azure Data Engineer
New
X
Xebia sp. z o.o.Data engineering
Workplace type: remote; Country code: PLFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- En B2
- Experience
- At least 8 years of professional experience in Data Engineering
- Required Skills
- PythonSQLMicrosoft AzureData engineeringDatabricks
Requirements
- At least 8 years of professional Data Engineering experience, including ownership of production-grade data pipelines.
- Strong, demonstrable hands-on experience with Microsoft Azure and Databricks.
- Advanced SQL and Python skills.
- Experience designing, building, and operating data pipelines and analytical data products.
- Knowledge of modern data-platform and lakehouse principles, including medallion architecture and dimensional modelling for reporting.
- Experience with software engineering and DataOps practices, including version control, automated testing, CI/CD, deployment, monitoring, and incident management.
- Experience with SRE practices and continuous operational improvements.
- Data modelling skills and the ability to consider different data scopes and business objectives when making build decisions.
- Ability to collaborate across Data, Architecture, Product, and Engineering teams.
- Ability to work autonomously, make technical decisions, and document them clearly.
- Nice to have: experience with Databricks data governance, lineage, and access control, including Unity Catalog.
- Nice to have: experience with payment or transaction data.
Responsibilities
- Design, build, test, and deploy scalable data pipelines for reporting and analytics.
- Create production-ready pipelines for priority reporting use cases.
- Review and refactor reporting pipelines to improve reliability, performance, maintainability, and observability.
- Maintain data pipelines to ensure data assets are delivered reliably each day.
- Work with Data professionals, Data Architects, Product Managers, and Engineering Managers to translate reporting needs into technical solutions.
- Onboard Core Products data workloads onto the Data Platform.
- Ensure pipelines meet Data Platform architecture, governance, security, and engineering standards.
- Own SRE practices for data pipelines and products; diagnose incidents, identify root causes, and implement corrective actions.
- Document data flows, dependencies, transformation logic, and operating procedures.
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