Top Data Engineer Jobs in Romania

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design, build, and maintain large-scale cloud-native data pipelines and lakehouse/warehouse solutions using Databricks, Spark/PySpark, Delta Lake and Snowflake. Collaborate with architects, product owners and data teams to implement ETL, data quality, governance, MLOps (MLflow) and BI integrations while ensuring best-practice CI/CD, version control and operational reliability.

Design and operate scalable, near-real-time data pipelines and backend services for a global payments platform. Model transactional ledger data for reconciliation, ensure data quality and observability, and own systems end-to-end in a remote-first environment.

Design, develop, and deploy production-grade ML and AI solutions including LLM fine-tuning, RAG architectures, NLP pipelines, time-series forecasting, supplier risk and anomaly detection, scalable data pipelines, and RESTful integrations with enterprise systems.

As a Senior DataOPS Engineer, you will architect and maintain reliable data platforms, ensuring performance and compliance while mentoring engineers and optimizing workflows across the organization.

Operate and maintain central Azure-based device usage data products with SLA compliance. Own production operations, incident handling, monitoring, governance, reproducible deployments, and integration of new device generations. Work hands-on with Databricks, PySpark, Unity Catalog, Azure DevOps, and Atlassian tools to ensure data quality, reliability, documentation, and controlled change.

The Data Engineer will design and optimize data pipelines in Azure and AWS environments, working with large datasets, building ETL/ELT processes, and creating data models to support analytics and reporting. Collaboration with business stakeholders and emphasis on data quality is key.

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.

The Data Engineer will design and maintain data pipelines, optimize ETL processes, ensure data quality, and collaborate in an agile environment.

Join the data infrastructure team as an ETL/Data Pipeline Specialist, monitoring data flows, building scrapers, and optimizing ETL architectures, working overnight.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.