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Design, build, and scale production-grade data platforms from scratch, covering batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.\nTechnical Leadership and Architecture: Lead system design discussions, review data schemas and architecture, conduct code reviews, and remain hands-on with Python and SQL.\nData Quality and Reliability: Own data quality checks, pipeline SLAs, production incidents, and root-cause analysis to ensure reliable data operations.\nData Ingestion and Integration: Oversee multi-source data ingestion, Change Data Capture (CDC), data lineage, data contracts, and cross-system data reconciliation.\nML and Analytics Enablement: Build and support data pipelines for ML training and evaluation, dataset versioning, feature pipelines, and human-in-the-loop feedback workflows.\nPerformance and Cost Optimization: Optimize cloud infrastructure and data processing costs while managing technical risks and communicating trade-offs to business stakeholders.\n\nRequirements: \nExperience: 9+ years of overall software or data engineering experience, including 6+ years of hands-on data engineering experience and at least 2 years of direct people management of data engineers.\nStrong Technical Foundation: Proven hands-on experience writing and owning production code in Python and SQL, with the ability to review code, evaluate schemas, and critique architectural designs.\nData Platform Ownership: Experience designing, building, and owning an end-to-end production data platform from the ground up, rather than simply maintaining an existing platform.\nTechnical Architecture: Strong understanding of data platform architecture, including batch and streaming pipelines, orchestration, and data warehouse/lakehouse infrastructure.\nProduction Reliability: Experience owning data quality checks, pipeline SLAs, production incident management, and root-cause analysis (RCA).\nPeople Management: Proven experience directly managing, 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