Top Data Engineer Jobs in Budapest

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.

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.

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.

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

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.

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.

Build scalable pipelines to ingest and process high-volume unstructured insurance documents, including PDFs, scans, emails, Word, Excel, and PowerPoint files. Responsibilities include OCR integration, document parsing, text normalization, semantic chunking, metadata extraction, enterprise connectors, retrieval-ready data preparation for RAG systems, validation, monitoring, logging, testing, and secure cloud deployment using software engineering best practices.

Lead design and development of backend APIs and front-end components for a Risk & Analytics platform. Implement FO/risk requirements, code, test, debug, monitor the platform, collaborate across teams, and mentor junior engineers. Support data visualization and integrate financial models and data pipelines for risk management and asset valuation.

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.

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.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Build and deploy data products on an AWS data mesh platform, onboard data from legacy warehouses, and develop streaming and batch pipelines using Flink, SQL, Python, Kafka, Iceberg, and DynamoDB. Collaborate with product engineering teams on data contracts, ownership, documentation, and platform adoption. Provide hands-on enablement, debugging support, technical guidance, and leadership for end-to-end initiatives.