Top Data Engineer Jobs in Prague

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 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.

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

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 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.

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.

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.

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.

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.

Build scalable data infrastructure and training pipelines to feed human-demonstration data into RAI. Train and evaluate deep-learning models, collaborate with RAI, hardware, and data teams, and deploy research results to production robots.

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, 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.

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.

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.

Design and build scalable backend solutions to collect and manage large data volumes. Implement business logic for Veeam core products, develop and enhance features, own major components, contribute to architecture, analyze requirements with product management, and estimate implementation effort.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Build scalable pipelines to ingest and process high-volume unstructured insurance documents, including PDFs, scans, emails, Word, Excel, and PowerPoint files. Responsibilities include OCR integration, document parsing, text normalization, semantic chunking, metadata extraction, enterprise connectors, retrieval-ready data preparation for RAG systems, validation, monitoring, logging, testing, and secure cloud deployment using software engineering best practices.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.