Top Data Engineer Jobs in Czechia

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.

Provide L2 application support for analytics and MDM platforms (Snowflake, SnapLogic, Boomi, Reltio), troubleshoot data pipelines using SQL and Python, drive incident resolution and root cause analysis, monitor platform health, support change and release activities, and maintain operational documentation. Work is primarily in-office in Prague.

Design, build, and own end-to-end data pipelines on BigQuery and dbt; orchestrate workflows with Dagster; enable analytics and AI use cases; build marketing attribution and reverse-ETL; enforce data quality, observability, and platform standards.

Build and scale a blockchain data platform handling petabytes of on-chain data. Design and implement distributed systems, data ingestion/decoding pipelines, and performant SQL processing. Write production code (Kotlin, Go, Java, Rust), improve developer experience, automate operations (including AI-driven automation), and own platform components.

Build and operate a scalable data platform enabling domain teams to self-serve. Implement Data Reliability Engineering (SLOs, data quality), optimize Snowflake costs, migrate legacy flows, and automate infrastructure with Terraform and AWS Fargate. Partner with warehouse, finance, and business teams to ensure production-grade, secure, and cost-efficient data systems.

The Software Engineer will manage the data ingestion pipeline, enhance infrastructure in GCP, and collaborate with scientists for improved data quality and scale.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

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.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Hands-on Senior Data Engineer who designs, builds, and maintains production data pipelines and transformations in SQL and Python. Works with cloud data platforms (AWS/GCP/Azure), Terraform-based CI/CD, Git workflows, and partners with DevOps and analytics teams to ensure reliable, testable, and compliant data workloads.

Design and implement ML models for personalization and predictions. Analyze data, research ML techniques, and collaborate with ML engineers for production integration.

Design, build, and optimize scalable data pipelines, models, and data-centric applications. Ensure data quality, governance, compliance, and performant database environments. Support BI, analytics, AI prototyping, automation, and cross-functional delivery while applying SDLC and design thinking.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Design, build, and deploy production-grade AI/LLM systems and agentic workflows. Implement prompt and context engineering, RAG, vector search, and LangGraph-based agents. Develop backend APIs, data pipelines, and monitoring for performance, scalability, and cost optimization while collaborating with product, data science, and engineering teams.

Develop, integrate, and support AI/LLM-powered applications and agent workflows. Assist with prompt engineering, context management (RAG/vector search), Python-based APIs and data pipelines, testing, monitoring, and collaboration with product and data teams under senior supervision.

Lead architecture, build, and deploy context-rich LLM and agent-based AI solutions. Design prompts, manage context/RAG and vector search, implement production Python services, integrate models and agents, ensure data pipeline quality, monitor and optimize agent performance, and guide cross-functional teams on scalable, secure AI deployments.

Lead and scale the Data Engineering function, overseeing data platform modernization, integration architecture, data warehousing strategy, and team growth while ensuring reliability, compliance, and optimized pipelines for business impact.

The Senior Data Engineer will design and build data pipelines, integrate ML models, optimize performance, and ensure data quality, while collaborating with traders and data scientists.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

The role involves managing data collection for AI model training, operating cloud infrastructure, and collaborating with the AI team to enhance datasets.

The role involves designing and implementing data entitlement systems, managing complex data architectures, and collaborating with data scientists to automate data governance in a market data platform.

The role involves designing and implementing entitlement frameworks for data platforms, ensuring secure access to market data, and leading governance for a market data platform using automation and AI.

The Senior Data Engineer will develop and maintain data architectures, ensuring data accuracy and security. They will collaborate on machine learning models and lead complex data processing initiatives.

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.