Feladatok

• Data Modeling: Design and maintain performance-optimized, denormalized structures for reporting and analytics.

• Pipeline Optimization: Build and refine transformation pipelines between core data layers and the reporting front-end.

• Standards & Governance: Define and enforce naming conventions, modeling guidelines, and engineering best practices.

• Technical Leadership: Mentor Analytics Engineers, conduct code reviews, and ensure high-quality output.

• Stakeholder Collaboration: Act as a bridge between Data Engineers and BI developers to translate requirements into robust data structures.

• System Improvement: Continuously scale and optimize analytical datasets for better maintainability.

Elvárások

• SQL: Advanced skills for complex transformations and performance tuning.

• DBT (data build tool): Extensive experience in managing transformation frameworks.

• Azure Synapse: Proven experience in cloud-based data warehousing.

• Power BI: Ability to design high-performance layers specifically for BI consumption.

• Data Architecture: Solid understanding of dimensional modeling and denormalization.

• Technical Leadership: Ability to make architectural decisions and lead a technical team.

Nice to Have

• Databricks: Experience with Spark-based processing is a significant plus.

• Modern Workflows: Familiarity with CI/CD, version control (Git), and automated testing of data models.

• Large Ecosystems: Experience working with diverse data consumers (Data Science, BI, and Finance).