Чем предстоит заниматься
Build Data Pipelines: Design and maintain robust, scalable ETL/ELT pipelines to ingest and process third-party and first-party datasets
Data Quality & Enrichment: Apply transformation, normalization, and enrichment rules to ensure data consistency and usability
Collaborate Across Teams: Work with product managers, data architects, and content experts to align data structure with business needs
Operationalize Matching & Merging Logic: Support the implementation of data matching and entity resolution processes using AI/ML tools and proprietary frameworks
Monitor & Troubleshoot Pipelines: Build alerts, logs, and metrics to ensure data flows remain healthy and issues are identified and resolved quickly
Dbt Development: Build and maintain data pipelines using dbt, developing models from raw data through staging and intermediate layers up to final semantic models for use in analytics and dashboards
Documentation & Standards: Contribute to documentation, code quality standards, and internal best practices to ensure maintainability