Top Data Engineer Jobs in Hungary

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Lead enterprise data migration engineering across discovery, metadata and permissions capture, SQL architecture, SSIS/ETL pipelines, and legacy-to-GCP migrations. Design migration strategies, cutover plans, rollback procedures, reconciliation, reporting, governance, and compliance solutions. Establish engineering standards, resolve complex issues, communicate risks, and coordinate delivery across business, infrastructure, security, and application teams.

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 optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

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.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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.