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 and maintain scalable data pipelines, warehouses, lakes, and cloud data platforms supporting analytics and business intelligence. Develop ETL/ELT workflows, optimize data models and databases, ensure data quality, security, governance, and reliability, and collaborate with IT, engineering, analysts, scientists, and business stakeholders. Contribute to dashboards, reports, data products, technical documentation, and data management best practices.

Designs and maintains analytics-ready datasets and data products for insurance and finance domains. Translates business processes into data models, metrics, and governed datasets; ensures quality, lineage, documentation, and discoverability. Uses AI-assisted analytics to accelerate exploration and decision support. Partners with business, governance, platform, analytics, and AI teams to deliver end-to-end data pipelines and enable self-service reporting and dashboards.

Lead and coach a data engineering team while building, testing, maintaining, and documenting data pipelines and APIs. Ensure reliable transactional and analytical data delivery through testing, controls, and engineering best practices. Collaborate with business, IT, and data stakeholders in agile ceremonies and design sessions. Apply Python, PySpark, SQL, CI/CD, Git workflows, distributed programming, data modeling, slowly changing dimensions, and change data capture techniques.

Designs, builds, and operates reliable ETL/ELT pipelines and analytics-ready datasets using Python, Azure Databricks, and SQL. Responsibilities include orchestration, monitoring, production support, performance tuning across SQL Server and PostgreSQL, data quality controls, and operational runbooks. The role also uses generative AI and integrates AI capabilities into enterprise data workflows while applying responsible AI principles such as privacy, fairness, and explainability.

Lead a data engineering team developing, testing, documenting, and maintaining data pipelines, services, and data artefacts. Ensure reliable transactional and analytical data delivery through testing, controls, and engineering best practices. Collaborate with IT, data, business, and international stakeholders through Agile ceremonies and design workshops. Support implementation of centralized standards and coordinate local delivery while contributing to APIs, CI/CD practices, and scalable distributed data processing.

Lead a data engineering team developing, testing, maintaining, and documenting data pipelines and API services for transactional and analytical needs. Apply data engineering best practices, testing controls, distributed programming, dimensional modeling, slowly changing dimensions, and change data capture. Collaborate across agile squads, coordinate local adoption of central standards, and communicate with business, data, and IT stakeholders.

Design, build, and optimize scalable Databricks data pipelines using PySpark, Python, SQL, and Delta Lake. Develop ETL/ELT processes, data quality controls, monitoring, and observability solutions. Integrate data platforms with Java and Spring Boot applications through REST APIs and microservices. Create reusable frameworks, support CI/CD, troubleshoot production issues, and contribute to architecture, modernization, and technical roadmap decisions while applying AI tools to improve delivery.

Designs and maintains analytics-ready datasets and data products for dashboards, reporting, AI, and decision-making across insurance or finance domains. Translates business processes into data models and reusable metrics, ensures quality, governance, lineage, and documentation, and uses AI-assisted analytics. Partners with business, platform, governance, analytics, and AI teams to deliver scalable, discoverable datasets from ingestion through consumption.

Design and implement cloud-native data platforms and lakehouse architectures on AWS. Build ETL/ELT pipelines, streaming systems, data services, and AI solutions including RAG and vector search. Apply data modeling, governance, orchestration, observability, and reliability practices. Collaborate with customers and internal teams on technical designs, modernization projects, troubleshooting, code reviews, performance tuning, and production support.

Develops and supports data warehouse and ETL solutions, including ODI packages, Oracle procedures, and Databricks jobs. Responsibilities include data mapping, source-system analysis, data modeling, testing, optimization, deployment, troubleshooting, database rollout coordination, and technical framework design. The role collaborates with business and IT stakeholders and requires advanced SQL and PL/SQL skills, large-scale database experience, performance tuning, and familiarity with data visualization tools. Python, PySpark, banking experience, and Agile knowledge are advantageous.

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.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures; integrate multiple data sources; optimize data quality, scalability, and performance; and support Power BI analytics. Collaborate with BI teams and contribute to cloud data architecture implementations under senior guidance.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures while integrating multiple sources. Ensure data quality, scalability, and performance, and collaborate with BI teams supporting Power BI and reporting platforms. Participate in cloud data architecture implementation under senior guidance.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Maintain SQL Server data platforms, ETL solutions, data models, and warehouse structures; integrate multiple data sources; optimize large-scale processing for quality, scalability, and performance; and support Power BI and reporting initiatives. Collaborate with BI teams and senior engineers while contributing to cloud-based data architecture implementation.

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.

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.

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.

Own the financial audit data workstream for Printful and Printify. Deliver accounting reconciliations, investigate transaction discrepancies, build Snowflake transformations, extend Looker models, collaborate with Accounting and auditors, and document pipelines, corrections, and edge cases against fixed deadlines.

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

Design and build cloud-native data platforms and lakehouse architectures on AWS. Develop and maintain ETL/ELT and streaming pipelines, support RAG/vector search and ML integrations, ensure data modeling, governance, observability, troubleshooting, and migration from legacy ETL to cloud warehouses while engaging with customers and stakeholders.

Design and build production data pipelines, integrate operational, contractual and financial clinical-trial data, create governed versioned data products and a source-of-truth using ontologies/knowledge graphs, and deliver trusted datasets for forecasting and downstream analytics.

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