• Design, build, and maintain the pipelines that power all data use cases. This includes ingestion of raw data from production databases, object storage, and message queues, and vendors into our Data Lake, and building core datasets and metrics. • Develop intuitive, performant, and scalable data models (facts, dimensions, aggregations) that support product features, internal analytics, experimentation, and machine learning workloads. • Implement robust batch and streaming pipelines using Spark, Python, and Airflow. • Build pipelines adhering to standards for accuracy, completeness, lineage, and dependency management. Build monitoring and observability so teams can trust what they’re using. • Work with Product, Data Science, Infrastructure, Finance, Marketing, and Sales to turn ambiguous questions into well-scoped, high-impact data solutions. • Pay down technical debt, improve automation, and follow best practices in data modeling, testing, and reliability. Mentor peers earlier in their career within the team.