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

Lead Data Engineer

• Support the implementation of a global real estate system (SAP / Planon) through hands-on data migration and data quality activities.
• Analyze source data provided by external vendors (e.g., CBRE), ensuring completeness, consistency, and usability.
• Define and execute data mapping between source and target systems, including both field-level and value-level mappings.
• Develop and apply data-driven transformation rules to align legacy data with target system requirements.
• Validate data loads and ensure overall data readiness for SAP / Planon deployment.
• Identify, document, and track data defects, inconsistencies, and missing inputs, ensuring timely follow-up and resolution.
• Support testing phases, including data validation, reconciliation, and issue resolution.
• Collaborate with global stakeholders while maintaining ownership of assigned data activities.
• Work independently with a high level of accountability, ensuring deliverables are completed without the need for close supervision.
• Participate in occasional on-site alignment meetings in Prague as required.

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Azure Databricks Data Engineer

Lead Data Engineer

Data Migration, Data Quality Specialist – Real Estate Systems