Senior Data Engineer (Azure/Databricks)

The role supports global customers mainly in the pharmaceutical and FMCG industry in building innovative big data products and services.

• 4+ years of experience in Azure and 6+ years of industrial experience in large-scale data management, visualization, and analytics
• Strong hands-on experience with Azure Databricks, including data engineering, data processing, and analytics workloads
• Hands-on knowledge of other Azure data services and technologies, such as Azure Data Lake, Azure SQL, Azure Data Factory
• Good knowledge of SQL and Python
• Proactive approach, goal-oriented mindset, and strong problem-solving skills
• Great communication skills for discussing technical issues with end users and clients
• Very good knowledge of English, with ability to communicate effectively and simplify technical topics for business
• Willingness to visit the Warsaw office as needed for customer visits, workshops, or important project periods

Nice to have:

• Experience designing and implementing CI/CD pipelines for data and analytics solutions, preferably using Azure DevOps, and data orchestration through Databricks Workflows
• Good understanding of DevOps practices, version control, automated testing, and deployment processes

• Designing, developing, and maintaining scalable data solutions in Azure Databricks for analytics and BI
• Managing the full data lifecycle, from acquisition and integration to analysis and visualization
• Building and maintaining CI/CD pipelines using Azure DevOps and/or GitHub, automating testing, deployment, and operations
• Delivering end-to-end data and analytics projects, from requirements and architecture to implementation and deployment
• Developing and optimizing data pipelines, visualizations, analytics products, automated services, and APIs
• Ingesting and integrating data from diverse sources into scalable, maintainable data platforms
• Ensuring performance, reliability, scalability, and data quality of data solutions
• Collaborating with multidisciplinary teams to turn data into models, insights, and production-ready solutions
• Identifying opportunities to leverage data and models to improve products and customer solutions
• Advising clients and stakeholders on data, Databricks, and solution architecture, identifying optimization opportunities