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.

Build and productionize agentic AI and NLP systems that process millions of documents and support data operations. Develop tool-calling agent loops, orchestration, memory management, RAG pipelines, metadata and entity extraction, and evaluation frameworks measuring accuracy, latency, and cost. Apply Python, SQL, and Snowflake to assess solutions, implement guardrails, manage AI risks, collaborate with technical stakeholders, and communicate delivery trade-offs.

Build and deliver production data pipelines, transformations, integrations, orchestrations, and AI-enabled workflows on the Keboola platform. Integrate client systems, model business context, create agent skills, and verify coding-agent output. Own scope, budget, documentation, handover, and client outcomes. Support presales through proofs of concept and technical presentations, advise existing accounts, and develop reusable components and workflows.

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 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.

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.

Owns production data integrations for an AI-assisted KYC and customer due diligence platform. Responsibilities include mapping data sources, defining data contracts and canonical models, building connectors, integrating Databricks with SQL Server and legacy systems, supporting APIs and SFTP workflows, implementing data-quality checks and contract tests, and validating reliable production data flows.

Build and operate production-grade Kafka-compatible streaming systems and Apache Flink applications. Develop Go services, manage event processing, state, checkpoints, schemas, and delivery guarantees, and deploy workloads on AWS and Kubernetes. Troubleshoot distributed production systems while improving reliability, scalability, performance, and resilience. Collaborate with engineers on platform evolution and apply AI-powered development tools and GenAI workflows.

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 operate Bloomreach’s Databricks-based data platform, including Kafka, Flink, Iceberg, Spark, and GCP data pipelines. Own data models, ETL, identity resolution, consent semantics, orchestration, monitoring, and data quality. Co-develop dashboards, canonical metrics, semantic layers, and analytics integrations. As a founding senior engineer, independently lead components, contribute to architecture, ship production improvements, and participate in on-call operations while using AI-assisted development tools.

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.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

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.

Develop and operate high-volume data pipelines processing real-time tracking, imports, transformations, and exports. Build reliable ETL systems using Python, Go, cloud data services, Kubernetes, and distributed processing technologies. Improve throughput, monitoring, autoscaling, and production reliability; support services handling tens of thousands of requests per second; review peer code; collaborate on complex projects; and eventually participate in on-call rotation.

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.

Own and continuously improve Creditstar’s AWS and Snowflake data platform, ensuring scalability, reliability, security, governance, observability, and cost efficiency. Establish engineering standards, manage integrations, support GDPR and PII controls, lead incident resolution, and collaborate with DevOps, BI, analytics, data science, risk, and product teams. The role also supports self-service data capabilities and FinOps practices.