Top Data Engineer Jobs in Budapest

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Support development of AI and perception solutions for camera and radar products used in autonomous driving and safety-critical vehicle systems. The intern will work with innovative software technologies, collaborate in an agile international team, and receive mentoring while contributing to autonomous mobility projects.

Design, develop, stabilize, and deploy Databricks applications for substation capacity management. Build Power BI dashboard functionality, integrate data sources, automate SAP/EAM interfaces, and implement data correction and quality processes. Collaborate with business and technical stakeholders to define requirements and deliver solutions.

Implement and evaluate a DX platform measuring the business value of AI-assisted software development. Integrate GitLab, CI/CD, ticketing, identity, SSO, and AI usage data; configure teams, permissions, data models, surveys, and measurement frameworks; ensure data quality, privacy, anonymization, and separation from individual performance monitoring; analyze quality, productivity, lead-time, and ROI metrics; and develop dashboards, documentation, and scaling recommendations.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build and deploy production-grade agentic AI systems, data pipelines, workflow orchestrations, APIs, and MCP integrations for large-scale financial and market-data operations. Develop secure, scalable applications and databases, implement CI/CD, automated testing, monitoring, and telemetry, and support deployed solutions. Collaborate with product, data, and infrastructure teams to automate data collection, validation, and quality assurance across multi-asset-class products.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Build and optimize data architectures, databases, warehouses, lakes, lakehouses, ETL processes, and data pipelines. Write SQL and Python, integrate data sources, develop Power BI and Superset reports and dashboards, and evaluate data technologies. Monitor performance, troubleshoot issues, ensure data security and compliance, and deploy infrastructure-as-code and Jenkins-based CI/CD automation while collaborating with engineering teams.

Design and maintain scalable data pipelines for structured and unstructured data supporting analytics, automation, and AI initiatives. Build data models, integrations, and quality controls; ensure reliable, secure, governed data across systems. Partner with AI engineers, product owners, and business teams to translate requirements into technical solutions. Monitor pipeline health, troubleshoot issues, improve performance, and support metadata, lineage, and documentation practices.

Design, build, and maintain scalable, production-grade data pipelines and models (ingest, transform, deliver) to enable BI, analytics, and AI. Translate business requirements from manufacturing, procurement, and supply chain into reusable data products, ensure data quality, monitor performance, manage deployments, use Git and AI-assisted tools, and collaborate with architects and downstream teams.

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 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.

Design, build, and optimize scalable Databricks Lakehouse solutions and Delta Lake pipelines across cloud platforms. Implement governance with Unity Catalog, automate deployments via DABs and CI/CD, write SQL/Python code, integrate APIs, and manage Databricks workspace automation. Own technical delivery components, collaborate with stakeholders, and create technical documentation.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Design and build large-scale distributed data systems for blockchain analytics. Translate ambiguous product requirements into technical designs, sequence work, make architectural decisions, and own components from design through production. Work across Go, Kotlin, Rust, and Python while optimizing data ingestion, distributed processing, and data lake storage. Mentor engineers, improve design reviews and testing practices, and collaborate effectively in a fully distributed team.

This role involves developing GenAI tools, building data pipelines, enhancing AI automations, and collaborating with stakeholders for technical solutions.

Manage and migrate ETL pipelines across source systems, validate pipeline builds and data quality, resolve pipeline issues and data discrepancies, support data migration, and provide operational support in Snowflake. Collaborate with business stakeholders and data subject-matter experts, maintain team best practices, document work, and track handovers.

Design and operate the data platform supporting AI products, including data models, storage architecture, APIs, backend services, ingestion pipelines, scoring workflows, and retrieval components. Ensure data correctness, performance, scalability, observability, and reliability across production systems. Collaborate with data scientists, AI engineers, and infrastructure teams to deploy services, resolve production issues, and safely manage schema changes and backfills.

As a Senior Data Engineer, you'll design and implement data pipelines, focus on AI-first workflows, manage data ingestion, and ensure data integrity.

Designs, builds, and maintains scalable batch and streaming data pipelines, lakehouse medallion architectures, transformation layers, and data models. Develops PySpark and SQL solutions, integrates diverse data sources, implements data quality monitoring, and manages infrastructure as code. Troubleshoots pipeline and data incidents, participates in code reviews, collaborates with architects, and mentors junior and mid-level engineers.