Top Data Engineer Jobs in Budapest

The Data Engineer will design and manage data workflows, develop AI-driven solutions, and maintain Snowflake data pipelines, improving analytics and reporting processes.

The Data Engineer will design, build, and maintain data pipelines, work cross-functionally to define data requirements, and ensure data is secure and governed while supporting AI initiatives.

Design, build, and operate large-scale batch and streaming data pipelines to ingest and process real-time maritime data. Implement data quality, governance, observability, and reliability tooling. Collaborate cross-functionally to deliver scalable, secure, and cost-efficient data solutions and participate in Agile 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.

Lead foundational data-engineering work to validate and re-engineer pipelines for an anonymized, centralized credit data lake. Harmonize schemas across entities, build dbt models and tests, implement data-quality suites (Great Expectations), entity resolution, anonymization controls, optimize Spark/Glue jobs, orchestrate pipelines (Airflow/Step Functions), and produce documented, feature-ready datasets and runbooks for a regulated UK/Ireland lending environment.

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.

Build, enhance and expand data pipelines to ingest and normalize large datasets for the European power market. Improve APIs, monitoring, alerting, performance and caching. Collaborate with data scientists and market experts to develop features, extend geographic coverage, and support product growth and reliability.

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

This role supports Supply Chain operations by improving data quality, managing data transformation, and designing data models for visualization.

The AI Systems & Data Engineer will handle data migration, build AI solutions, integrate systems, and automate workflows for HR transformation.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

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 Platform Engineer will optimize data infrastructure, build ELT pipelines, and collaborate with various teams to enhance data governance and insight-driven decision-making.

Maintain and operate the Power Platform frontend (Power BI, Power Apps, Power Automate, SharePoint) integrated with an Azure Lakehouse. Ensure reliable data connections, gateway management, RBAC and security, monitor performance and refreshes, resolve incidents, coach Self-Service BI teams, produce operational dashboards and documentation, and drive continuous improvement and alignment with backend data engineering standards.

The Senior Cloud Data Engineer designs and implements data solutions on AWS, optimizing data pipelines, writing SQL and Python code, and managing deployments with DevOps practices.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Lead development of Data and AI features from data architecture to optimization and simulation. Design scalable data architectures, reliable pipelines, and DataOps/DevOps workflows. Partner with analysts, product managers and supply chain specialists to turn complex logistics data into actionable solutions.

Build and optimize data pipelines in Snowflake, automate transformations, and collaborate with technical and business teams to meet project requirements.

The Senior DBA/Data Platform Engineer will ensure the reliability, performance, and operational quality of SQL Server databases, supporting critical business applications, and collaborating with engineering teams for production stability and disaster recovery.

The Principal Software Engineer will design and develop scalable applications for energy and water management, optimize performance, and lead systems architecture as part of a senior technical team.

Design, build, and own streaming data pipelines and feature stores to enable low-latency inference and real-time model serving. Partner with ML and infra teams to define data contracts, ensure data quality, observability, and SLAs, and move batch pipelines toward real-time production. Evaluate streaming and feature store technologies and contribute to inference pipeline tooling.

Build and maintain real-time streaming data pipelines and feature stores to power low-latency model inference. Collaborate with ML and AI infra teams on data contracts, observability, and SLAs; drive batch-to-real-time migrations and evaluate emerging streaming/feature store technologies.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.