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.

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.

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 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.

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.

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.

Design and build the company’s internal data infrastructure, pipelines, and BI tools. Stabilize integrations, eliminate manual workflows, establish documentation and standards, deliver self-service dashboards, and enable cross-functional analytics and P&Ls. Use AI coding assistants to accelerate development and ownership of end-to-end data solutions.

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

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

Lead foundational data-engineering work to validate and re-engineer pipelines for an anonymized, centralized credit data lake. Harmonize schemas across entities, build dbt models and tests, implement data-quality suites (Great Expectations), entity resolution, anonymization controls, optimize Spark/Glue jobs, orchestrate pipelines (Airflow/Step Functions), and produce documented, feature-ready datasets and runbooks for a regulated UK/Ireland lending environment.

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.

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.

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

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

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.

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.

The Data Engineer will build and maintain systems for data collection, storage, and analysis, ensuring data accuracy and security. Responsibilities include creating data pipelines, data warehouses, and collaborating with data scientists on machine learning models.

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

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.

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.

Build and maintain real-time streaming data pipelines and feature stores to power low-latency model inference. Collaborate with ML and AI infra teams on data contracts, observability, and SLAs; drive batch-to-real-time migrations and evaluate emerging streaming/feature store technologies.