Top Data Engineer Jobs in Romania

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

As a Junior Data Engineer, you'll design and implement data pipelines, improve ETL processes, manage analytics platforms, and communicate with stakeholders.

The Manager of Data Science will lead a team focused on AI innovations utilizing LLMs, provide mentorship, and align technical strategies with business goals.

The Senior Data Engineer will design and maintain data pipelines, develop ETL processes, optimize GCP services, ensure data quality, and collaborate with stakeholders.

Design, build, and maintain cloud-based data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate data from multiple sources, optimize large-scale processing, support SQL Server/SSIS environments, and collaborate with BI teams for analytics and reporting.

Design and develop Azure Databricks-based data pipelines and Lakehouse architectures using PySpark, Spark SQL, Delta Lake, and Azure Data Factory. Integrate and optimize data from multiple sources, support SQL Server/SSIS platforms, ensure data quality and scalability, and collaborate with BI teams for analytics and reporting.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate data from multiple sources, optimize large-scale processing, support SQL Server/SSIS platforms, and collaborate with BI teams for analytics and reporting.

Design, develop, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources, optimize large-scale transformations for performance, support SQL Server/SSIS environments, and collaborate with BI teams for analytics and reporting.

The Data Engineer designs, implements, and monitors security controls for cloud and on-premises infrastructure, ensuring resilience against cyber threats and compliance with regulations while collaborating with teams.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build and maintain cloud-based data lakehouse pipelines using SQL-driven transformations. Harmonize, model, and prepare data for analysis, reporting and AI. Ensure data availability, integrity, security, monitoring and performance. Collaborate with cross-functional teams, share knowledge, and adopt modern Data Engineering and DevOps practices.

Design, build, and optimize low-latency, high-throughput real-time data pipelines and event-driven architectures. Implement, monitor, and maintain streaming processing systems, ensure data quality and reliability, integrate multiple data sources, collaborate with platform and ML teams, support troubleshooting and operational excellence, and mentor junior engineers.

Own and improve customer and product data pipelines, CDP and analytics platforms (Segment, Mixpanel, BigQuery, dbt). Build analytics-ready models, replace fragile integrations, enforce data governance, QA, and monitoring, and partner with engineering, product, marketing, and sales to enable downstream reporting and activation.

The Data Engineer will build and maintain data pipelines, ensure data quality, and support advanced analytics with a focus on AI-driven use cases.

Develop and maintain ETL/ELT processes, data platforms, and big-data pipelines that feed live European operations. Treat data as production code, enable data modeling and orchestration, and can grow into ML engineering within an AI track.

The Senior Product Data Engineer will build, iterate, and launch data products, focusing on large-scale data processing and ETL/ELT pipelines, using various technologies and implementing AI features.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Design, build, and operate scalable cloud-native data pipelines and infrastructure to support analytics and AI workloads. Develop dimensional and semantic data models and curated data products, optimize cloud data services, maintain versioned development workflows, and collaborate with AI, Analytics, and Product teams to deliver high-quality data for LLMs, agents, and RAG solutions.

Develop, enhance, and maintain server-side product data and API services using Java/Spring Boot. Implement REST web services, ensure resiliency and high availability, add monitoring, troubleshoot issues, participate in code reviews, and maintain technical documentation.

As a Software Engineer for Data Infrastructure & Acquisition, you will manage data collection for model training, extend cloud infrastructure, and collaborate with AI scientists on datasets.

As a Software Engineer in Data Infrastructure, you'll handle data collection for model training, manage GCP infrastructure, and enhance data ingestion pipelines.