Top Data Engineer Jobs in Budapest

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.

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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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.

Lead technical design and improvements for a large-scale identity data platform. Prototype and evaluate pipeline changes for cost, performance, and reliability; build consensus and drive implementation; mentor engineers; integrate with broader Mastercard systems and support ML workflows.

Design, build, and operate a cloud-native data platform on Kubernetes; support Spark-based processing, platform reliability, AWS and Snowflake integrations, and AI/GenAI initiatives while collaborating with data scientists and engineers.

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.

Design, build, and deploy scalable Databricks Lakehouse solutions and modular data pipelines using Delta Lake. Implement governance with Unity Catalog, automate deployments via DABs and CI/CD, write Python/SQL code, integrate cloud storage and security, and manage technical delivery and documentation.

Design, build and maintain scalable ELT/ETL data pipelines and the Data & Analytics platform. Integrate and transform internal and external sources, define logical and physical data models, ensure high data quality, monitor and resolve pipeline incidents, document processes, and advise on architecture and best practices including lakehouse and big data capabilities.

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.

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.

Serve as senior technical lead for cloud data solutions and lakehouse architectures: align data requirements with business needs, support pre-sales and estimations, architect data models/platforms, lead testing, mentor 3–6 team members, and guide data warehouse/lakehouse migrations.

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.

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 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.

This role involves data collection for model training, maintaining cloud infrastructure, collaborating with scientists on data quality and cost, and developing an AI dataset roadmap.

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.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

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, develop, and operate scalable BI data pipelines and reporting solutions integrating customer, booking, and product data from multiple APIs. Deliver datasets to downstream systems (Data Warehouse, Billing, Kibana), ensure data quality and performance, support AWS-based operations, and help shape a new BI engineering team and technical standards.

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, 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.

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.