Top Data Engineer Jobs in Prague

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.

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.

The Data Engineer will design data pipelines and architecture, optimize machine learning models, and collaborate on data projects for international brands.

The Data Engineer designs big data architecture, optimizes machine learning models, collaborates on data projects, and builds data pipelines.

Design, build, and deploy large-scale distributed systems and high-throughput data pipelines. Lead architecture and technical initiatives, ensure performance, resilience, and SLAs, contribute production-quality code, and mentor engineers while collaborating with product and R&D leadership.

Design, build, and operate large-scale distributed systems and high-throughput data pipelines using Go and cloud-native technologies. Lead architecture, performance optimization, reliability SLAs, and code quality. Drive technical roadmap, mentor engineers, reduce technical debt, and collaborate with product and engineering teams.

Design, build, and deploy large-scale distributed systems and high-throughput data pipelines using Go and cloud-native technologies. Lead architecture decisions, ensure performance and resilience, contribute high-quality code, reduce technical debt, and mentor engineers while collaborating with product and engineering teams.

Design, build, and own large-scale distributed systems and high-throughput data pipelines. Lead architecture, improve performance and resilience, reduce technical debt, contribute code, and mentor engineers while collaborating with product and R&D.

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.

Design and build the knowledge layer for AI agents that represent brand identity and reasoning. Develop agentic search, dynamic retrieval, and RAG systems; evaluate retrieval/reasoning architectures; translate AI research into production systems; collaborate with product and AI teams to improve user-facing outcomes.

As a Data Engineer, you will manage integration between Snowflake and various operational tools, ensuring data accuracy and availability for Sales, Marketing, and Product teams. Responsibilities include building reliable data pipelines, designing CDP layers, and resolving pipeline incidents.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with 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.

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.

Design and build real-time data pipelines using ClickHouse and Google Cloud. Focus on data quality and optimize financial datasets for analytics.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Design and implement AI projects, balance technical complexity with business impact, and collaborate with stakeholders to deliver value using AI and data.

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

Design and implement core YTsaurus functionality in C++, build and evolve platform-level capabilities, improve APIs and UX for internal users, own production quality including incident response/on-call, and collaborate with stakeholders to prioritize and deliver scalable data platform features.

Design and develop a big-data processing engine and heavy ETL pipelines (including geo-location analysis) across cloud and on-prem. Build distributed, partitioned pipeline architectures using Spark/PySpark, Scala, Airflow, Kubernetes, and cloud platforms; follow Agile, clean-code, and software design best practices while supporting deployments and client-facing interactions.

Design and maintain scalable data pipelines and platforms, lead migration from Snowflake to client service, propose improvements, drive initiatives, and collaborate with senior engineers, product/project managers, and data analysts.

Lead design and implementation of data platforms and pipelines for a large online car marketplace. Simplify and modularize tools, address technical debt, propose improvements, and collaborate with senior engineers, product and engineering managers. Work with AWS Glue/Athena, Python/PySpark, SQL, Airflow and streaming frameworks (Kafka/Firehose) on a Canadian time-zone schedule.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.

Design, validate and oversee mechanical systems for data centers. Create cost-effective mechanical designs and construction documentation, coordinate with internal teams and external vendors, test installations, analyze performance, and visit sites to supervise builds and ensure quality.