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.

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 maintain scalable data manufacturing pipelines using Databricks and PySpark; integrate new energy data sources; modernize ETL processes; and drive LLM/GenAI integration into extraction and transformation workflows while collaborating with engineering, QA, and product teams.

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.

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.

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.

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

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 maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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.

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

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

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.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory / Microsoft Fabric to integrate APIs, databases, and files into Azure Data Lake and Azure SQL. Ensure data quality, validation, logging, monitoring, CI/CD, performance tuning, and production troubleshooting to support enterprise analytics and Power BI reporting.

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.

Develop, support, and optimize ETL/ELT pipelines for high-volume enterprise data using Databricks, Snowflake, Kafka, and Spark. Build batch and streaming ingestion, implement data quality, reconciliation, lineage, and observability. Automate ingestion, testing, deployment, and monitoring; participate in code reviews and collaborate with senior engineers to deliver production-grade, performance-aware data solutions.

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.

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.

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.

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.

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.