San Francisco, California3 days ago
Evaluate and adopt modern data infrastructure - including real-time streaming (Kafka, Flink), columnar engines (DuckDB, ClickHouse), lake house, and cloud-native object storage architectures; Foster a culture of collaboration, innovation, and continuous improvement; Provide technical guidance and mentorship to team members, promoting their professional growth; Conduct performance reviews, provide feedback, and identify opportunities for training and development; Manage team workload, prioritize projects, and ensure timely delivery of high-quality solutions. Stakeholder Management: Partner with data scientists, data engineers, lab scientists, product managers, and other stakeholders to understand their data processing needs and requirements; Communicate technical concepts and solutions effectively to both technical and non-technical audiences; Advocate for best practices in data processing and engineering; Manage expectations and ensure alignment across different teams.