Top Data Engineer Jobs in Czechia

As a Data Engineer, you will develop data solutions for global banks, modernizing data processes and designing architectures while leading sub-projects.

As a Data Engineer, you will develop data solutions, optimize processes, design data architectures, and lead small project teams for banking clients.

The Data Engineer will develop and maintain data pipelines, implement data transformation models, and enhance the data platform, collaborating with various stakeholders.

As a Data Engineer Manager, you'll lead a team in creating scalable data systems for trading strategies, ensuring high performance and reliability in data processing.

As a Data Engineer, you'll design, implement, and maintain data pipelines, collaborate with data teams, and optimize workflows to ensure data quality for trading initiatives.

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

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

The Data Engineer will design and implement data pipelines and architectures, support machine learning model deployment, and engage in data management and optimization strategies for big data environments.

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

Design and operate data processing layers for Zeta's AdTech platform, focusing on streaming and batch data pipelines. Collaborate with multiple teams to support ML features and analytics systems.

As a Data Engineer, you will build Python applications for data processing, contribute to platform improvements, and support internal users. You'll work with data lakes and ETL tools in an agile environment.

As a Lead Software Engineer, you will design and build scalable data pipelines, lead technical execution, and improve platform quality. You will collaborate with data scientists and product managers, address technical debt, and mentor other engineers.

The Data Engineer will design and implement big data architectures and pipelines, optimize machine learning models, collaborate with stakeholders, and automate data processing using cloud services and CI/CD tools.

Design, build, and optimize scalable Azure data pipelines and Lakehouse solutions using Azure Data Factory and Databricks. Integrate varied data sources, apply business logic, migrate legacy databases to cloud, and collaborate in Agile teams to deliver high-quality analytics-ready datasets.

The Data Engineer will build and maintain data pipelines, develop a scalable Data Lake on AWS, and collaborate with cross-functional teams to enhance data solutions.

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 Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

Design, build, and own scalable data pipelines and an analytical data warehouse (e.g., Snowflake). Ensure data quality, monitor operational health, and partner with BI and ML teams to deliver reliable data solutions.

Design and maintain data architectures, build data pipelines, and communicate with stakeholders to enhance data reliability and quality.

The Senior Data Engineer will design and build data pipelines, structure data, implement event tracking, and ensure data quality for a no-code platform.

As an Applied ML Engineer, you will build and scale data pipelines for video generation, ensuring data quality and optimizing preprocessing workflows.

Develop and maintain scalable data architecture and pipelines, ensuring data quality for analytics and business decision-making in collaboration with stakeholders.

As a Big Data Engineer, you will design and implement data processing systems, manage technologies like Hadoop and Spark, and build ETL processes to ensure data quality and performance.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

As a Staff Data Engineer, design and implement large-scale data processing systems, build data pipelines, and architecture with a focus on scalability, performance, and security while collaborating across teams and mentoring juniors.