Top Data Engineer Jobs in Czechia

Design, develop, and optimize data architectures for agile financial services projects. Responsibilities include business analysis, functional design, data engineering, solution design, data modeling, and implementation of data lakes, warehouses, and other storage solutions. The role uses SQL, Python, cloud platforms, and big-data technologies while progressively taking ownership of architecture topics, leading smaller workstreams, and contributing to internal initiatives.

Design, develop, and optimize data architectures for agile financial-services projects. Responsibilities include business and functional analysis, conceptual data modeling, data engineering, solution design, and implementation using cloud, data lake, data warehouse, and enterprise database technologies. The role gradually assumes ownership of topics, leads smaller workstreams and teams, and contributes to internal initiatives.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

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, 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 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.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

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.

Design, build, and maintain scalable data manufacturing pipelines using Databricks and PySpark; integrate new energy data sources; modernize ETL processes; and drive LLM/GenAI integration into extraction and transformation workflows while collaborating with engineering, QA, and product teams.

Design and implement data models and complex dbt/SQL transformations for migrating and consolidating enterprise and laboratory data into an AWS platform. Query distributed sources with Trino/Presto, integrate systems such as SAP, GLIMS, and Veeva, validate data quality, optimize pipelines, and maintain lineage and documentation. Collaborate with architects, analysts, engineers, and pharmaceutical stakeholders in an Agile environment, supporting testing, code reviews, and CI/CD.

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.

Design, develop, test, deploy, and maintain databases, services, and automated data pipelines. Write maintainable Python and database code, support production platforms, analyze requirements and data, and collaborate with product, design, and engineering teams in an Agile environment. The role also involves architecture, CI/CD, customer-focused solutions, and modern workflow orchestration tools.

Lead the design, implementation, and operation of enterprise data platforms, scalable ELT/ETL pipelines, master data structures, lakehouse and semantic models, and Power BI reporting. Own a cost-management data solution, establish data quality and governance controls, and implement DevOps practices. Partner with finance, operations, and business stakeholders to deliver secure, trusted, high-performing data products and provide technical leadership across architecture, deployment, and support.

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.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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.

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

Design and maintain data architectures, build data pipelines, and communicate with stakeholders to enhance data reliability and quality.

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.

Design, implement and integrate safety-critical avionics software across the full lifecycle: requirements, architecture, coding (C/C++), unit testing, V&V, certification and in-service support. Collaborate with systems and V&V engineers, contribute to process improvements, and work in a hybrid team using Atlassian toolchain.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.