Top Data Engineer Jobs in Hungary

Design, build, and maintain scalable ETL/ELT pipelines and data warehouses for programmatic ad-tech. Ingest and normalize SSP/DSP/GAM log-level data, implement data models and quality monitoring, support BI and analytics, and ensure GDPR/CCPA compliance.

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.

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.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design and build scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, and transform bid stream, impression, and revenue data; maintain data warehouses; implement data quality monitoring and privacy compliance; support BI teams and develop analytics and models for yield optimization and partner performance.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Operate and govern Power Platform frontend for a cloud Data & Analytics platform: maintain Power BI/Power Apps/Power Automate, manage gateways and RBAC, monitor performance and data refreshes, coach Self-Service BI teams, ensure security and compliance, collaborate with platform engineers, and drive continuous improvements and automation.

Lead design and delivery of cloud data solutions and lakehouse architectures. Align data requirements with business needs, support technical pre-sales, plan testing, and mentor/lead project teams (3-6 members) through data warehouse/lakehouse migrations and implementations.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

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 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.

Design, build, and maintain ETL/ELT pipelines and data models integrating multiple enterprise systems. Develop Power BI semantic models, DAX measures and automated reports. Implement Python-based automation, API integrations, AI/ML-enabled analytics, and workflow orchestration while ensuring data quality, lineage, and governance. Collaborate with cross-functional teams to define data strategy and automation opportunities.

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.

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.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.