Senior Data Engineer - Data Integration Engineer

Senior Data Engineer - Data Integration Engineer

Job Role: Senior Data Engineer / Data Integration Engineer

Skill: Data Integration

Skill Level: Master

Location: Bucharest / All Romania 

Role Overview

We are looking for an experienced Senior Data Engineer / Data Integration Engineer to design, develop, and maintain scalable data platforms and integration solutions using Microsoft Azure and Databricks.

The role focuses on building robust data pipelines, data lake and warehouse solutions, and enterprise data integrations that support reporting, analytics, AI, and Agentic AI use cases. The candidate will also be responsible for ensuring data quality, security, governance, performance, and reliability through engineering best practices and monitoring.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes using Azure and Databricks.
• Build and optimize Data Lake, Delta Lake, and Data Warehouse solutions.
• Develop data solutions supporting:
• • Reporting
  • Business analytics
  • Advanced analytics
  • AI use cases
  • Agentic AI workflows
• Integrate data from multiple enterprise systems using:
• • Azure Data Factory
  • Databricks
  • APIs
  • Cloud-native Azure services
• Design reliable and scalable data integration architectures.
• Develop data transformation and processing workflows using Databricks.
• Ensure data products are trusted, consistent, and fit for business and AI-driven use cases.
• Implement engineering best practices for data quality, security, governance, and performance.
• Implement monitoring and operational frameworks for data pipelines and integration processes.
• Troubleshoot data pipeline and integration issues and perform root-cause analysis.
• Optimize data processing and pipeline performance.
• Collaborate with data scientists, AI teams, application teams, and business stakeholders.
• Support Agentic AI initiatives by preparing, transforming, and delivering data required by AI-powered workflows and intelligent applications.

Required Technical Skills

• Strong experience in Data Engineering and Data Integration.
• Strong hands-on experience with Microsoft Azure.
• Strong hands-on experience with Azure Databricks.
• Experience designing and implementing scalable ETL/ELT pipelines.
• Strong experience with Azure Data Factory (ADF).
• Experience with Data Lake architectures.
• Strong knowledge of Delta Lake.
• Experience designing and implementing Data Warehouse solutions.
• Experience integrating data from multiple enterprise systems.
• Experience with REST/API-based data integration.
• Experience with cloud-native data services.
• Strong understanding of data quality and data validation.
• Knowledge of data security and governance principles.
• Experience with data pipeline monitoring and operational frameworks.
• Strong understanding of data performance optimization and scalability.

AI & Advanced Analytics

• Experience preparing and transforming data for advanced analytics and AI.
• Understanding of data requirements for AI/ML workflows.
• Experience delivering trusted data products for business and AI applications.
• Understanding of Agentic AI data requirements and AI-driven workflows.
• Ability to collaborate with AI/ML teams to provide reliable and appropriately structured data.

Professional Skills

• Strong analytical and problem-solving skills.
• Ability to design scalable and maintainable data solutions.
• Strong troubleshooting and root-cause-analysis capabilities.
• Good communication and stakeholder-management skills.
• Ability to work effectively with distributed, multidisciplinary teams.
• Strong ownership and attention to data quality.
• Ability to work in fast-paced enterprise environments.

Nice to Have

• Experience with Azure-based AI/ML services.
• Experience with Microsoft Fabric.
• Experience with Azure Synapse Analytics.
• Experience with advanced Databricks architectures.
• Experience with data governance frameworks and enterprise data catalogs.
• Experience with CI/CD and DataOps practices.
• Experience with Infrastructure as Code.
• Experience supporting large-scale cloud data transformation programs.