1. Data Platform Development
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Design, implement, and optimize scalable data storage and processing solutions on Azure (e.g., Azure Data Lake, Azure Data Factory , Azure SQL,, Azure Databrick, Microsoft Fabric).
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Align development efforts with the Data Platform product roadmap and architectural standards.
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Contribute to the evolution of our Data Platform into a robust provider for data consumers, including reporting systems, internal applications, and other analytical services.
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Continuously improve and optimize the Data Platform to ensure peak performance, scalability, and reliability across all data use cases.
2. Data Integration & ETL/ELT
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Develop reliable data ingestion pipelines using Azure Data Factory, Microsoft Fabric, Azure Databricks
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Implement efficient ETL/ELT processes for both batch and streaming data sources.
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Using (T)SQL, interpret & transform data into business ready data stores for analytical use
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Understand, improve & create data models (semantic layer) for use in Power BI
3. Monitoring & Operational Support
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Monitor, troubleshoot, and improve performance of data pipelines and storage systems.
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Implement logging, telemetry, and alerting for proactive system maintenance and operational resilience.
4. DevOps & CI/CD
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Contribute to CI/CD pipelines using tools like Azure DevOps or GitHub Actions.
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Ensure seamless integration of data components within agile product teams.
5. Stakeholder Collaboration
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Work closely with Data Owners, Stewards, Data Architects, BI Analysts, and Governance Leads to ensure consistent application of data standards and policies.
6. Workflow Automation
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Identify and automate recurring data workflows using scripting languages (e.g., Python (PySpark), PowerShell) and Azure-native automation tools (Logic Apps).
7. Data Governance & Quality
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Apply best practices for data security, access control (e.g., Azure Purview, RBAC), and quality checks.
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Ensure compliance with organizational data governance frameworks.
8. Documentation & Standards
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Maintain clear, up-to-date documentation of data pipelines, data models, and architecture decisions.
Ensure alignment with enterprise data architecture standards.
