Senior Data Engineer
Microsoft
Senior Data Engineer
Prague, Czech Republic
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Overview
In this role, you will build robust data workflows leveraging technologies such as Azure Data Factory, Synapse, Microsoft Fabric, and Spark. You’ll implement advanced transformation logic using SQL, Python, SCOPE or KQL, optimize for performance and cost, and integrate CI/CD practices for automated testing and deployment across multiple cloud environments. Beyond pipeline development, you will champion DataOps best practices, establish monitoring and alerting systems, and lead root-cause analysis to drive continuous improvement in reliability and efficiency.
Success in this position requires strong problem-solving skills, a passion for data-driven decision-making, and the ability to collaborate across engineering, analytics, and compliance teams. You will influence platform strategy, mentor peers, and ensure that our data architecture supports both current and future business needs. This is a high-impact opportunity to shape the foundation of our data ecosystem and enable insights at a global scale.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Qualifications
Required Qualifications:
- Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND experience in business analytics, data science, software development, data modeling, or data engineering
- OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field experience in business analytics, data science, software development, data modeling, or data engineering
- OR equivalent experience.
- Customer-facing, project-delivery experience, professional services, and/or consulting experience.
Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND experience in business analytics, data science, software development, data modeling, or data engineering
- OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND experience in business analytics, data science, software development, data modeling, or data engineering
- OR equivalent experience.
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Responsibilities
- Design, implement, and maintain end-to-end data pipelines using Azure Data Factory (ADF), Microsoft Synapse, Microsoft Fabric, and Spark.
- Develop scalable data transformation logic using Structured Query Language (SQL), Python, and Structured Computations Optimized for Parallel Execution (SCOPE) for large-scale datasets.
- Automate orchestration, scheduling, and dependency management with built-in retry and recovery mechanisms.
- Integrate Continuous Integration and Continuous Deployment (CI/CD) pipelines for automated testing, performance validation, and multi-cloud deployments.
- Implement comprehensive observability, including logging, tracing, and real-time monitoring dashboards for data workflows and platform services.
- Design logical and physical data models, enforce schema standards, and ensure data quality, security, and compliance across structured and semi-structured data.
- Collaborate with analytics, product, and machine learning teams to deliver high-quality, scalable solutions and mentor peers in data engineering best practices.
- Define and evolve schemas for structured and semi-structured data (e.g., Structured Streams, Parquet, Delta Lake, JSON).
- Implement dimensional modeling (star/snowflake) for analytical workloads.