Top Data Engineer Jobs in Bucharest

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, test, optimize, and maintain data warehouse ETL solutions. Build ODI packages, Oracle stored procedures/packages, and Databricks jobs; map and profile source data; support rollouts and troubleshoot ETL processes; define technical frameworks and collaborate with stakeholders and project teams.

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 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.

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 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.

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.

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.

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.

As a Consultant at Deloitte, you'll design and implement technology solutions for large companies, driving business value through innovative software development.

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.

The Senior Data Engineer will develop scalable ETL pipelines, write maintainable Python code, optimize data transformation processes, and utilize Google Cloud services while collaborating in an agile team.

As a Senior Data Engineer, you will design and implement scalable data solutions, develop automated pipelines, and support cloud transition using AWS and Snowflake.

As a Big Data Platform Engineer, you will manage and optimize Cloudera Data Platform clusters, automate workflows, and collaborate with teams to enhance data operations.

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.

Develop and maintain low-latency data platform components and feed handlers using Java; implement automated testing and monitoring; collaborate with Product Owner and cross-border teams; learn KDB/q and financial markets concepts.

Design, implement, and manage scalable cloud-native batch and real-time data pipelines. Process structured and unstructured data, optimize ETL/ELT and data models, evaluate new tools, collaborate with data scientists and analysts, automate Unix-based operations, and document best practices.

Design, build, and operate Azure-based data platform environments and lead migrations to Azure/Databricks/Microsoft Fabric. Implement IaC (Terraform), Kubernetes platform operations, CI/CD automation, security and governance controls, monitoring/observability, and cost/performance optimizations. Collaborate with data engineers, architects, and stakeholders and produce technical documentation while providing technical leadership during the migration.

Build and optimize scalable ETL/ELT pipelines, design data models, ensure data quality and security, improve pipeline/query performance, implement CI/CD for data workflows, and collaborate with product and analytics teams to enable data-driven decisions.