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.

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.

Design, build, and maintain data solutions using PySpark and Power BI. Own small projects end-to-end, work with business users to gather requirements, validate solutions, support testing and troubleshooting, and ensure delivered solutions meet business needs.

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.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

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

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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.

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.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate and optimize data from SQL Server sources, ensure data quality and performance, and support BI/reporting teams (Power BI). Evolve and support legacy ETL (SSIS) while contributing to modern cloud data platform design.

Design, develop, and maintain scalable data pipelines and Lakehouse architectures using Azure Databricks, PySpark/Spark SQL, Delta Lake, and Azure Data Factory; integrate and optimize SQL Server-based data warehouses, ensure data quality and performance, and support BI/reporting (Power BI).

Design, build, and maintain scalable data pipelines and Lakehouse architectures on Azure Databricks. Develop PySpark and Spark SQL transformations, orchestrate ETL/ELT with Azure Data Factory, integrate data sources, optimize performance, support SQL Server/SSIS environments, and collaborate with BI teams for analytics and reporting.

Design, build, and maintain scalable data pipelines and Lakehouse architectures on Azure Databricks using PySpark, Delta Lake, and Azure Data Factory; integrate SQL Server sources, optimize performance, ensure data quality, and support BI/reporting (Power BI).

Design, build, and operate production ETL/ELT data pipelines and FastAPI services to deliver curated drug, prescription, and clinical datasets. Ensure data quality, lineage, observability, CI/CD and schema management. Collaborate with product and AI teams to enable AI-native features and partner integrations, leveraging AI tools like Claude Code.

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 and build scalable data pipelines and models for scientific instrument data; implement custom parsers for diverse file formats; create unit/integration tests and utility libraries; analyze datasets to design shared data model components; produce visualizations, reports, and interactive dashboards.

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.

Assess and redesign multi-store reporting architecture; design scalable, high-performance analytics and real-time/near-real-time reporting solutions; define data modeling and event enrichment strategies; implement efficient unique-count and aggregation approaches; recommend technologies and trade-offs; support migration and ensure reporting consistency; drive data quality, observability, and performance optimizations; mentor and guide engineering teams.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

The Data Engineer will build and optimize data pipelines, integrate data sources, enhance analytics platforms, and collaborate with teams to deliver data solutions.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory / Microsoft Fabric to integrate APIs, databases, and files into Azure Data Lake and Azure SQL. Ensure data quality, validation, logging, monitoring, CI/CD, performance tuning, and production troubleshooting to support enterprise analytics and Power BI reporting.

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