Top Data Engineer Jobs in Hungary

Design, implement, and maintain backend systems, data pipelines, storage, and APIs for a SaaS platform. Ensure platform availability, data quality, security, testing, automation, and documentation. Support customers and internal staff, contribute to sprint planning and estimations, and track project progress.

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.

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.

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.

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

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.

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

Build and maintain CI/CD pipelines, infrastructure automation, Kubernetes-based data platform environments, and observability tooling. Manage Terraform and Helm deployments, cloud cost optimization, provisioning, policy enforcement, incident response, capacity planning, and operational documentation. Collaborate with data engineering and security teams to improve reliability, scalability, compliance, and engineering productivity across enterprise lakehouse platforms.

Deploys, operates, and extends LinkedIn DataHub across cloud and on-premises environments. Integrates lakehouse and BI platforms, manages metadata ingestion, builds lineage graphs, implements classification and stewardship workflows, and develops custom plugins and APIs. The role also monitors platform performance, translates governance policies into controls, and trains data stewards and consumers.

Design and implement enterprise data quality frameworks for lakehouse platforms. Build automated validation tests, dashboards, anomaly detection, observability solutions, and CI/CD quality checks. Manage data quality SLAs, profile new sources, resolve incidents, collaborate with governance and source owners, and educate engineering and product teams on quality standards.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Design, build, and optimize cloud-based Databricks data platforms and scalable ETL/streaming pipelines (Delta Lake, Spark). Collaborate with data scientists and stakeholders, troubleshoot performance, and support analytics and MLOps workflows on Azure.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

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.

Design and evolve an Azure-based Lakehouse architecture using Databricks and Delta Lake. Develop sustainable platform architectures and streaming/data pipelines, prototype solutions, evaluate new technologies, coach engineering teams, ensure data protection/security, and establish architectural principles, patterns, and best practices to support enterprise analytics and migration from traditional data warehouses.

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.