Top Data Engineer Jobs in Prague

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.

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 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.

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 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.

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 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.

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.

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.

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.

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.

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.

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.

Develop Python-based data processing, analysis, modeling, forecasting, and visualization tools. Build and optimize Snowflake data pipelines for ingesting, cleaning, transforming, and loading large datasets. Prototype and deploy end-to-end data operations, develop Streamlit applications, and collaborate with technical and analyst teams to gather requirements. The role supports market and vendor data products and may involve data quality checks, outlier detection, machine learning, and generative AI.

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.

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.

Lead the architecture and hands-on delivery of an Everything-as-Code data mesh, including Kafka and NiFi event streaming, Kubernetes infrastructure, Terraform and GitOps deployment, schema governance, hybrid lakehouse integration, FinOps optimization, resiliency, and distributed observability across multi-cloud and on-premises environments.

Build and maintain ETL pipelines, embedding pipelines, document processing infrastructure, and semantic search capabilities for an AI-powered intelligence platform. Improve indexing, ranking, retrieval accuracy, and data quality while supporting AI agent queries and customer-uploaded documents. Monitor pipeline performance, resolve data issues, and develop reliable infrastructure for accurate search results.

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.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.