Top Data Engineer Jobs in Bucharest

Design, build, and maintain scalable cloud-based data pipelines and ETL processes on Azure (Databricks, ADF, Synapse, ADLS). Implement data models, quality checks, governance, and performance optimization. Collaborate with architects, cloud engineers, data scientists, and stakeholders to consolidate legacy services and enable data-driven insights.

Design, build, and maintain scalable real-time and batch data pipelines for programmatic ad-tech. Ingest and normalize SSP/DSP/GAM data, develop ETL/ELT workflows, data models for yield and auction analytics, maintain warehouses/lakes, implement data quality monitoring, and ensure GDPR/CCPA compliance while supporting BI and analytics teams.

Build and maintain high-performance market data infrastructure in Rust to ingest, process, store, and serve real-time pricing feeds. Solve latency, data quality, and cross-exchange consistency problems, contribute trading/market-data expertise to engineering decisions, communicate with a distributed team, and potentially lead a small team over time.

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

The Staff Data Platform Engineer will architect and engineer a cloud-native data platform on AWS, focusing on ETL/ELT pipelines, automation, monitoring, and data governance while collaborating with various teams and mentoring other engineers.

The Senior Data Analytics Engineer will design and maintain analytical pipelines, write SQL for large datasets, utilize AI tools, and communicate insights to stakeholders.

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.

Design and implement scalable Azure data platforms, develop data pipelines, and support clients with data modernization and governance practices.

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.

Design, develop, and maintain microservices for data ingestion, focusing on scalability and reliability using Java and various big data technologies.

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 maintain scalable AWS-based data platforms and pipelines ingesting from DynamoDB, Aurora PostgreSQL, and Neptune; implement Iceberg-based S3 data lake zones; develop CDC and batch ingestion, Airflow orchestration, Glue Data Catalog integration, CI/CD, automated testing, and monitoring; optimize Iceberg table design and troubleshoot production issues.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

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.

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.

Conduct 60-minute technical interviews for senior data engineering candidates, evaluate cloud-based data platform architecture, ETL/ELT pipelines, distributed processing, security, data modelling, and communication. Complete scorecards, provide hiring recommendations, and collaborate with recruiters to improve assessments.

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.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

The Qlik Sense Developer will create applications, manage data modeling, support internal users, and collaborate with teams focused on data visualization.

The Data Engineer will enhance, test, and implement applications, support IT operations, and collaborate with stakeholders to ensure delivery of robust solutions.

The Data Analytics Engineer designs analytics-ready datasets, translates business needs into analytical models, ensures data quality, and collaborates with stakeholders for effective data governance.