Top Data Engineer Jobs in Budapest

Build and maintain data pipelines and benchmarking visualizations using Python. Collaborate with data scientists and engineers to support analytics platform and R&D digital transformation. Contribute to testing, CI, and cloud-native/data warehouse workflows within an agile team.

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.

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 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 AWS-based data solutions and pipelines. Write maintainable SQL, Python, and shell scripts. Containerize with Docker, implement CI/CD (GitHub Actions/GitLab CI), automate pipelines, use AWS data services (S3, Redshift, Glue, EMR, Lambda), perform data modeling/warehousing, lead component-level deliveries, liaise with stakeholders, and create technical documentation.

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.

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.

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

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.

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.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

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.

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.

As a Senior Data Engineer, you'll deliver complex projects using Apache Spark/Databricks, manage customer relationships, mentor junior engineers, and contribute to team success.

Design and implement data architectures, lead projects, collaborate with stakeholders, mentor teams, and ensure code quality.

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.