• Design, build, and operate AI-ready data platforms leveraging Snowflake and cloud-native services • Architect and deliver conversational Data & AI Assistants integrated with Slack, Gemini, and enterprise systems • Partner closely with data analysts and business SMEs to translate business questions into reliable data products and AI experiences • Design and implement LLM-powered integrations and RAG-based data access patterns • Ensure availability, performance, scalability, and resiliency of data, analytics, and AI platforms • Define and implement operational standards including support models, SLAs, escalation paths, and incident lifecycle processes • Serve as a technical leader and mentor, setting engineering best practices and raising the bar across teams • Collaborate with data engineering, AI/ML, cloud, security, product, and business stakeholders • Champion SRE, DevOps, and FinOps best practices through hands-on implementation and guidance

• Develop and maintain data pipelines and ETL processes using Python in AWS environments • Write and deploy AWS Lambda functions for data processing tasks • Assist in designing and implementing data integration solutions from various source systems • Collaborate with team members using Git for version control and code collaboration • Write efficient SQL queries for data extraction, transformation, and validation • Participate in code reviews to learn best practices and improve code quality • Document technical processes, data flows, and system configurations • Troubleshoot data quality issues and implement validation checks • Support the monitoring and maintenance of production data pipelines • Continuously learn new technologies and AWS services to expand technical capabilities

• support the building and maintaining of data pipelines, transformations, staging processes, and star-schema data models. • support ELT/ETL processes in Snowflake, SQL Server, and related tools to ensure timely and accurate data availability. • monitor data workflows and assist with troubleshooting and resolving pipeline or data issues. • assist with automating data ingestion from institutional systems including ERP, CRM, LMS, and other sources. • develop and maintain data tables, views, and transformations that support analytics and reporting needs. • assist in building and maintaining data marts and reusable datasets following established modeling standards. • identify and help resolve data integrity issues across integrated systems (ERP, CRM, and LMS platforms). • perform data validation and quality checks to ensure accuracy and consistency. • maintain documentation for data pipelines, transformations, and processes as directed. • collaborate with the Data & Analytics team to support data needs across the University. • participate in cross-functional projects requiring data integration or data preparation. • learn and apply best practices related to SQL development, data modeling, and data warehouse operations. • contribute to continuous improvement efforts within the Data & Analytics team.

• Design, optimize and own data pipelines that scrape, process and ingest transaction and listing data from major auction houses and marketplaces. • Build comprehensive monitoring and alerting systems to track latency, uptime, and coverage metrics across all data sources. • Continuously improve our data infrastructure by modernizing storage and processing technologies, reducing manual interventions, and optimizing for cost, performance, and reliability. • Partner with internal teams to understand data usage patterns and ensure pipelines deliver clean, standardized data that meets product requirements.