Top Data Engineer Jobs in Bucharest

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.

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

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.

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.

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.

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.

Design, build, and maintain scalable data pipelines on GCP; optimize data models; ensure data quality; support legacy data migration; collaborate with teams; implement monitoring; contribute to data governance best practices.

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.

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.

Lead and deliver end-to-end corporate data pipelines and analytical datasets, ensuring reliability, scalability, data quality, lineage, and governance. Provide hands-on technical leadership, design large-scale data processing solutions, coach data engineers, collaborate with business stakeholders across group functions, and drive enterprise data standards and AI-ready practices to support corporate reporting, KPIs, and regulatory needs.

As a Big Data Platform Engineer, you'll manage and optimize Cloudera Data Platform clusters, ensuring performance and scalability for ING Wholesale Banking's data infrastructure, while automating operations and supporting collaboration across data 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.

Build, maintain, and scale ELT/ETL data pipelines and dbt models on Google BigQuery. Write Python for data processing and automation, ensure data quality, monitor pipelines, troubleshoot issues, and collaborate with analytics, product, and game teams to deliver reliable analytics datasets and contribute to data architecture.

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 scalable data pipelines on Databricks for analytics and AI/ML. Implement ETL/ELT, medallion-style models, CI/CD and orchestration, ensure data quality, governance, security, performance, and troubleshoot production issues while collaborating with cross-functional teams.

Design, develop, and maintain scalable, metadata-driven ingestion microservices and pipelines on the Cloudera platform. Build and support data workflows using Spark, Hive, NiFi, HDFS, Kafka and DataStage; integrate with Oracle/RDBMS. Implement CI/CD, monitoring (Kibana), automation via Bash, participate in code reviews and testing, and uphold software engineering, DevOps, and data governance best practices to ensure reliable production delivery.

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 scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Operate and automate the data plane for a sovereign Google Cloud region: manage enterprise storage (SAN/FC/iSCSI), PostgreSQL at scale, Kubernetes CSI/stateful workloads, backup/DR pipelines, and infrastructure automation. Drive durability, availability, recoverability, incident response, and capacity planning while participating in follow-the-sun on-call with Google Cloud SRE teams.

Design and deliver analytics-ready datasets and data products for a business domain (P&C, L&H, or Finance). Translate business needs into data models, ensure data quality and governance, document lineage and metrics, and collaborate with stakeholders and platform teams to enable discoverable, self-service analytics.

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.

Design and build a cloud-native data platform using Snowflake, dbt, Fivetran, and AWS. Migrate legacy SQL Server/SSIS/SSAS systems, develop and optimize ELT pipelines and SQL models, implement data quality, governance, monitoring and lineage, automate workflows with Git and CI/CD, and collaborate with global stakeholders to enable analytics and AI use cases.