Top Data Engineer Jobs in Czechia

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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 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 and implement backend systems, ETL pipelines, data storage, and APIs for a SaaS platform. Maintain platform availability and improve security, quality, testing, automation, documentation, and features. Support customers and staff, contribute to sprint planning and prioritization, estimate development effort, track project progress, and proactively resolve roadblocks.

Design, build, deploy, and own production data pipelines on Databricks using Spark Declarative Pipelines, PySpark, Delta Lake, and Apache Iceberg. Implement CI/CD, data quality, monitoring, lineage, governance, and performance optimization across environments. Translate ambiguous business requirements into technical designs, develop reusable frameworks, support production incidents, and mentor engineers. Collaborate with product managers, architects, and stakeholders in a regulated enterprise 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.

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

Designs and delivers scalable Java backend platform capabilities for discovering, classifying, and protecting sensitive data. Responsibilities include distributed-system development, integrations with data sources and storage systems, performance and concurrency optimization, testing, deployment, troubleshooting, code reviews, and architectural improvement. The role collaborates with engineering, product, and customer-facing teams and requires working primarily in the Prague office.

Lead a software engineering team to design and deliver clinical data services for trials: ingest, transform, harmonize, and deliver regulatory-grade data using cloud-native architectures, CI/CD, and SDLC practices. Shape technical vision, collaborate with product and architecture, mentor engineers, and ensure secure, compliant solutions for submissions.

Designs, develops, and improves scalable software and data integration solutions using modern cloud data technologies. Responsibilities include collaborating with product and engineering teams, conducting code reviews, applying secure coding practices, implementing unit tests, managing risks and controls, and advising stakeholders on complex technical issues. The role requires AWS data services, Databricks, MS SQL, and prior data integration experience, with optional experience in Agile product ownership, solution architecture, business analysis, C#, and .NET.

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.

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.

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.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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.

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 Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.