Top Data Engineer Jobs in Czechia

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Design and scale the data platform powering analytics, product insights, and machine learning. Lead data architecture, modeling, ETL, reliability, observability, quality, and query optimization initiatives. Integrate product data sources, support reporting and BI access, improve engineering practices, mentor teammates, and provide technical guidance on platform evolution. Collaborate with product, engineering, and analytics teams to prioritize work, assess impacts, and resolve ecosystem issues.

Design, develop, test, deploy, and maintain databases, services, and automated data pipelines. Write maintainable Python and database code, support production platforms, analyze requirements and data, and collaborate with product, design, and engineering teams in an Agile environment. The role also involves architecture, CI/CD, customer-focused solutions, and modern workflow orchestration tools.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build, maintain and monitor data and AI pipelines for Precision Forestry. Develop new ETL/processing solutions, troubleshoot production issues, optimize performance, and collaborate with AI leads, data engineers and business stakeholders to ensure reliable, high-quality data delivery.

Design, build, and maintain scalable data pipelines, services, and backend components; integrate AI/LLM features (chatbots, RAG, copilots) using OpenAI/Anthropic/LangChain; implement APIs, cloud deployments (AWS/Azure), CI/CD, monitoring, and support; collaborate with architects and cross-functional teams in Agile to translate business requirements into enterprise data and AI solutions.

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 scalable data models and pipelines for scientific instrument data. Implement custom file parsers, unit/integration tests, and reusable utilities. Perform dataset comparisons, create visualizations and dashboards (Streamlit, Tableau, Jupyter), and collaborate with product and engineering teams to deliver AI-enabled data solutions.

Designs and builds scalable Azure data pipelines, integrates structured and unstructured sources, and provisions data into Lakehouse architectures. Develops with Azure Data Factory and Databricks, implements batch and streaming processing, applies business logic, optimizes large datasets, and supports legacy database migrations to cloud platforms. Collaborates in Agile teams to deliver trusted data solutions for analytical users.

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

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

Design, develop, integrate, and test avionics software across the full lifecycle for aerospace systems. Work on embedded C/C++ applications, collaborate with systems and V&V engineers, support certification and in-service operations, and contribute to process and product improvements in a hybrid team environment.

Lead design and implementation of scalable knowledge graph solutions for pharmaceutical R&D. Build graph data models, integrate heterogeneous scientific datasets, drive adoption of RDF/property-graph technologies, partner with platform and AI/ML teams, ensure performance and governance, define roadmap, and mentor engineers in ontology and graph engineering best practices.

Design, implement and integrate safety-critical avionics software across the full lifecycle: requirements, architecture, coding (C/C++), unit testing, V&V, certification and in-service support. Collaborate with systems and V&V engineers, contribute to process improvements, and work in a hybrid team using Atlassian toolchain.

Provide hands-on Apache Airflow engineering and customer enablement: develop and troubleshoot DAGs, integrate Airflow with data ecosystems and cloud services, support production onboarding, resolve distributed system issues, deliver technical guidance and workshops, and collaborate with customers and internal teams to stabilize and operate a managed Airflow platform.

Design, build, and maintain scalable data manufacturing pipelines (Databricks/PySpark) to ingest, standardize, and deliver energy and geoscience data. Integrate GenAI for extraction, consolidate overlapping datasets, mentor juniors, and define engineering standards while ensuring CI/CD, orchestration, and cloud best practices.

Lead and mentor a data engineering team to build and maintain scalable ETL pipelines (Databricks/PySpark), integrate large energy and geoscience datasets, standardize pipelines, drive LLM/GenAI integration for data extraction, coordinate with product management, and influence platform standards and tooling.

Design, build, and operate real-time ingestion and transformation pipelines (dbt, SQL, Python). Create dimensional models and semantic layers for analytics, ensure data quality and observability, optimize warehouse performance, support dashboards and client reports, and collaborate with analysts and stakeholders to deliver trusted BI insights.

Design and build end-to-end data pipelines on Snowflake using a medallion architecture; develop dbt transformations and tests; implement automated data validations; create Streamlit UIs for user inputs and checks; prepare Power BI reporting outputs; document and hand over solutions to clients while collaborating with stakeholders.

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

Design, build, and maintain the internal data warehouse and scalable data pipelines. Integrate data from multiple sources, develop and maintain Snowflake data models, configure Workato integrations, ensure data quality and performance, make data accessible for reporting and analytics, and contribute to DWH architecture and best practices while collaborating with stakeholders.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

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

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.