Top Data Engineer Jobs in Budapest

The Data Engineer will design and maintain data pipelines, collaborate with cross-functional teams, and implement data quality measures for machine learning models.

The Data Engineer designs and implements data infrastructure, ETL/ELT processes, manages data pipelines, ensures data quality, and supports data-driven decision making.

As a Data Engineer at Zapier, you'll build scalable data systems, enhance product functionality through data, and collaborate with teams to improve data access and usability.

The Senior Cloud Data Engineer designs and implements data solutions on AWS, optimizing data pipelines, writing SQL and Python code, and managing deployments with DevOps practices.

As a Principal Database Engineer, you will architect scalable PostgreSQL solutions, ensuring data reliability, performance, and collaborating with teams to build a robust data platform for GitLab's growth.

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

As a Middle Data Engineer, migrate SQL Server data warehouses to Snowflake, refactor ETL processes, build data pipelines, and ensure reliable data movement.

As a Data Engineer at Screenverse, you'll build ETL pipelines, ensure data quality, and contribute to data strategy in a collaborative environment.

Build and optimize data pipelines in Snowflake, automate transformations, and collaborate with technical and business teams to meet project requirements.

N-iX seeks a Data Engineer with MLOps experience to create scalable ML pipelines, deploy models, and contribute to best practices in data engineering and coding.

As a Data Engineer, you'll design, develop, and implement high-performance data infrastructure and pipelines while communicating effectively with various stakeholders.

The Data Engineer will design and maintain ETL pipelines, optimize data models, develop exporters, and ensure reliable data services for the Lambda app in a fast-paced startup environment.

Design, build, and optimize scalable data platforms using Azure and Databricks. Responsible for developing ETL/ELT pipelines, maintaining data quality, and collaborating with analytics teams.

As a Data Engineer, you'll design and develop features for a new Data Platform utilizing AWS, data processing tools, and machine learning concepts. You'll collaborate with cross-functional teams, ensure quality deliverables, and leverage AI tools for coding and debugging.

As an Analytics Engineer, you will design and maintain SQL models for on-chain data, work with transformation tools, and collaborate with engineers on data pipelines.

Design and architect scalable backend services, develop robust governance systems, build SaaS solutions, and mentor junior engineers to enhance IoT platform capabilities.

As a Data Engineer, you'll design and maintain ETL pipelines, write TypeScript code, and collaborate with stakeholders to ensure data quality and performance.

As a Senior Data Engineer, you'll analyze data to provide insights, manage ETL processes, and collaborate for data strategy development.

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.

As a Data Engineer, you'll maintain and configure the Snowflake data platform, design integration pipelines, and innovate with new tools. Your role supports data security and performance while collaborating across teams.

The Junior Data Engineer will analyze data, design experiments, build a marketing data stack, and collaborate with teams to improve data access and insights.

Lead data platform design and evolution, optimize ETL workflows, ensure data quality, mentor engineers, and partner with business teams.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

Drive the development of Data and AI features, design scalable data architectures, ensure reliability and accuracy of data solutions, shape DevOps workflows, and collaborate with analysts and product managers to transform complex data into practical 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.