Top Data Engineer Jobs in Budapest

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.

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.

Contribute to development and improvement of Azure cloud data solutions for the Risk domain using Databricks, Delta Lake, SQL and Python. Support CI/CD, DevOps automation, unit testing, and collaborate with stakeholders to build scalable, secure data products.

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 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.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

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

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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.

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.

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.

Design, build, and operate scalable Azure/Databricks ingestion and CDC pipelines; maintain Delta Lake bronze/silver datasets; develop production-grade Python ingestion frameworks with CI/CD, testing, and packaging; implement observability, security, and governance; mentor engineers and define platform standards.

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.

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.

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

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.

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.

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

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.