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About the role
Role Overview We are looking for a Data Platform Reliability Engineer to join our Global Data team. This role focuses on enabling reliable, scalable, and self-service data platforms built on modern cloud technologies such as Google BigQuery Lakehouse architectures, and distributed data pipelines. You will play a key role in supporting production data platforms, improving platform observability, and empowering users through automation, AI-driven capabilities, and self-service solutions. Key Responsibilities - Leverage AI/GenAI tools to improve data discovery, metadata generation, and operational efficiency - Build and enhance data observability frameworks (data freshness, data quality, pipeline health) - Monitor and troubleshoot data pipelines, ingestion, and transformation workflows - Enable self-service data access for business users through well-defined datasets, documentation, and tools - Automate repetitive support tasks using scripting and platform-native capabilities - Collaborate with engineering, analytics, and business teams to resolve data issues and improve platform usability - Provide support for enterprise data platforms, ensuring high availability and reliability - Support data governance initiatives, including metadata, lineage, and cataloging - Participate in incident management, root cause analysis, and continuous improvement efforts Required Skills & Experience - 34 years of experience in data engineering / data platform support - Strong hands-on experience with Google BigQuery, GCS, Composer - Experience working with data lakehouse architectures (e.g., BigQuery, DBT) - Valuable understanding of data ingestion and transformation tools (e.g., Airflow, Composer, Informatica CDC, dbt, or similar) - Proficiency in SQL and Python - Experience with cloud platforms (GCP preferred; AWS/Azure is a plus) - Familiarity with data pipeline monitoring and troubleshooting - Basic understanding of data governance and metadata management tools (e.g., Collibra or s .
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