Data Engineer

• Work on the core engine behind a production AI assistant
• Focus on retrieval, LLM orchestration, and answer quality
• Evolve AI-powered customer support from answering questions to executing requests
• Safely perform actions like placing orders or activating services in a regulated telecom environment

AI Data Engineer

• Design and implement data ingestion and processing of various data sources using public cloud services.
• Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs, like optimizing existing data delivery, re-designing infrastructure for greater scalability, etc.
• Collaborate with Business Intelligence consultants and assemble large and complex data sets that meet functional and non-functional business requirements for data lakehouse.
• Support machine learning teams in deployment and optimization of AI / Machine Learning models and other data algorithms.
• Develop data pipelines to provide actionable insights into marketing automation, customer acquisition and other key businesses areas.
• Use infrastructure as a code and CI/CD tools to automate release automation pipelines.
• Document data pipelines and logic in a structured manner using Confluence, plan your activities using Agile methodology in Jira.
• Support pre-sales by proposing technical solutions and accurate effort estimation.

Senior Data Engineer

• Design, build, and maintain scalable batch and streaming pipelines using bronze/silver/gold architectures
• Develop reusable transformation frameworks and production-grade Spark workloads
• Implement and optimize Delta Lake solutions, including: ACID transactions, Partitioning strategies, Schema evolution, OPTIMIZE/ZORDER, Incremental and CDC processing patterns
• Configure and manage Unity Catalog permissions, governance, lineage, and auditability
• Perform Spark performance tuning and observability monitoring
• Integrate Databricks with Azure services including ADLS, ADF/Synapse, Key Vault, and Microsoft Entra ID
• Implement CI/CD pipelines and deployment automation using Azure DevOps or GitHub Actions
• Collaborate with architects, analysts, data scientists, and engineers to align technical implementation with business needs
• Continuously improve platform performance, reliability, cost efficiency, and standardization

Azure Databricks Data Engineer

• Architect & Lead Operational Data Flows by designing and overseeing the implementation of an Operational Data Store (ODS)
• Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within customer-facing applications
• Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows
• Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency
• Modernize transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow)
• Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data
• Build the "Data Foundation" for Machine Learning, including development of Feature Stores and automated pipelines for model training and inference
• Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default
• Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value

Data Engineer jobs in Czechia

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AI Data Engineer

Senior Data Engineer

Azure Databricks Data Engineer

Lead Data Engineer