Data & ML Infrastructure Design: Architect and implement scalable infrastructure for data and machine learning workloads, leveraging modern cloud-native and on-premises distributed systems.
Data Pipeline Engineering: Build and maintain robust, high-performance data pipelines to ingest, process, and store large-scale datasets from diverse sources.
Database & Warehouse Optimization: Administer and fine-tune databases and data warehouses to ensure high availability, data integrity, and optimal performance.
Data Integration: Consolidate data from various origins—APIs, internal systems, and third-party providers—into unified, analysis-ready datasets.
Self-Service Enablement: Develop tools and interfaces that empower business users and analysts to independently access insights and interact with ML models.
Platform Growth & Innovation: Contribute to the evolution and scaling of the Data & AI Platform, supporting new use cases and capabilities.
Security & Compliance: Uphold data governance standards, ensuring compliance with privacy regulations and implementing robust security practices.
Data Quality Management: Establish validation and monitoring mechanisms to maintain data accuracy, consistency, and reliability across systems.
Cross-Functional Collaboration: Partner with data scientists, analysts, and engineering teams to understand platform needs and deliver tailored infrastructure solutions.
Monitoring & Optimization: Implement observability tools and performance tuning strategies to ensure system reliability, scalability, and cost-efficiency.
Documentation & Knowledge Sharing: Maintain clear, comprehensive documentation of data workflows, APIs, schemas, and ML systems to support onboarding and collaboration.