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, 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.

As a Data Engineer, you will manage integration between Snowflake and various operational tools, ensuring data accuracy and availability for Sales, Marketing, and Product teams. Responsibilities include building reliable data pipelines, designing CDP layers, and resolving pipeline incidents.

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.

As a Data Engineer Manager, you'll lead a team in creating scalable data systems for trading strategies, ensuring high performance and reliability in data processing.

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 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 scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Lead foundational data-engineering work to validate and re-engineer pipelines for an anonymized, centralized credit data lake. Harmonize schemas across entities, build dbt models and tests, implement data-quality suites (Great Expectations), entity resolution, anonymization controls, optimize Spark/Glue jobs, orchestrate pipelines (Airflow/Step Functions), and produce documented, feature-ready datasets and runbooks for a regulated UK/Ireland lending 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.

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 Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

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

Provide L2 application support for analytics and MDM platforms (Snowflake, SnapLogic, Boomi, Reltio), troubleshoot data pipelines using SQL and Python, drive incident resolution and root cause analysis, monitor platform health, support change and release activities, and maintain operational documentation. Work is primarily in-office in Prague.

Build and operate a scalable data platform enabling domain teams to self-serve. Implement Data Reliability Engineering (SLOs, data quality), optimize Snowflake costs, migrate legacy flows, and automate infrastructure with Terraform and AWS Fargate. Partner with warehouse, finance, and business teams to ensure production-grade, secure, and cost-efficient data systems.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

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.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Develop and own core data protection features focused on tape storage and ransomware detection. Design and implement business logic, build new features, contribute to architecture, work closely with databases, and solve low-level multithreading challenges to ensure reliability and performance.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Lead and scale the Data Engineering function, overseeing data platform modernization, integration architecture, data warehousing strategy, and team growth while ensuring reliability, compliance, and optimized pipelines for business impact.

The DevOps Engineer will design, implement, and optimize cloud and hybrid infrastructure solutions for big data and automotive projects, ensuring scalability, security, and compliance while automating deployment processes.

Design, build, and own streaming data pipelines and feature stores to enable low-latency inference and real-time model serving. Partner with ML and infra teams to define data contracts, ensure data quality, observability, and SLAs, and move batch pipelines toward real-time production. Evaluate streaming and feature store technologies and contribute to inference pipeline tooling.