Top Data Engineer Jobs in Czechia

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.

Build and maintain data pipelines and models, then develop AI assistants, applications, and automations for business teams. Experiment with AI coding tools, deploy solutions to production, and help colleagues adopt AI. Collaborate with senior data and AI engineers while learning to scope business problems, shape real data, and present results.

Design, develop, test, and maintain enterprise integration and data engineering solutions using SAP Integration Suite, Databricks, APIs, databases, and cloud technologies. Build data pipelines, apply AI-enabled data quality and validation capabilities, and integrate applications across complex enterprise environments. Collaborate with AI experts and project teams to translate requirements into scalable solutions while developing expertise in SAP integration and modern data technologies.

Build and deploy Snowflake data pipelines using Python, Snowpark, and Apache NiFi. Curate governed data assets with metadata, lineage, and ownership definitions; transform raw data using SQL and dimensional modeling; optimize queries and warehouse performance; and collaborate with data engineers, data scientists, and analysts to support analytics and AI workloads.

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.

Designs and maintains scalable data pipelines and architectures supporting analytics, machine learning, Generative AI, LLM, RAG, and semantic search applications. Responsibilities include modernizing and migrating legacy data platforms, developing AI-ready datasets, implementing data governance and quality controls, supporting feature stores and model-serving pipelines, and building reusable data products. The role also drives automation, security, compliance, observability, and collaboration across engineering, data science, product, architecture, and business teams.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Build and operate enterprise-scale data pipelines and AI-powered data platforms for customer analytics, marketing, e-commerce, and partner management. Responsibilities include data integration, data enrichment, MLOps, automation, monitoring, DevOps, incident resolution, and maintaining reliable cloud data infrastructure across Databricks, Snowflake, and Azure. The role also requires translating business needs into technical specifications and supporting large-scale production data systems.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build AI-native data engineering agents, multi-agent workflows, semantic layers, evaluation frameworks, monitoring, and reliable data pipelines. Develop dbt and Snowflake models supporting revenue, marketing, and product analytics while improving data quality, testing, documentation, and deployment practices. Collaborate with Analytics, BizOps, Product, and Engineering teams to deliver self-service AI-enabled analytics.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Design and develop scalable backend solutions for collecting and managing large volumes of data across Veeam’s cloud, SaaS, virtualization, backup, and replication products. Build business logic, develop product features, own major components, contribute to architecture decisions, establish requirements with product management, and estimate implementation effort, costs, and capacity.

Designs, builds, and optimizes scalable data pipelines, integrations, models, databases, and data-centric applications. Ensures data quality, governance, compliance, security, and performance while supporting business intelligence, analytics, visualization, and reporting. Collaborates with product teams, data scientists, and stakeholders to develop innovative digital capabilities, including AI/ML prototypes. Contributes to software design, automation, code reviews, and continuous improvement in a compliance-driven environment.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Design and build cloud-based big data architectures, ingestion and processing pipelines, lakehouse datasets, and scalable data infrastructure. Collaborate with architects, data scientists, BI consultants, stakeholders, and clients. Support machine learning model deployment, automate infrastructure and CI/CD processes, document pipelines, and contribute to pre-sales solutioning and effort estimation. The role serves international clients and supports marketing automation, customer acquisition, and other business intelligence initiatives.

Design and implement cloud-based big data architectures, ingestion and processing pipelines, lakehouse datasets, and infrastructure automation. Optimize data delivery and scalability, support machine learning model deployment, and develop pipelines for marketing and customer acquisition insights. Collaborate with architects, data scientists, BI consultants, stakeholders, and clients while documenting solutions, using Agile methods, supporting CI/CD, and contributing to pre-sales technical proposals.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

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.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.

The Senior Product Data Engineer will build, iterate, and launch data products, focusing on large-scale data processing and ETL/ELT pipelines, using various technologies and implementing AI features.

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.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.