• 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.

• Design, build, and maintain scalable and resilient data pipelines using Spark, Presto, and SQL • Develop high-quality ETL workflows to ingest, transform, and aggregate data from multiple upstream sources • Partner closely with data analysts, finance partners, and product teams to understand business needs • Design and evolve data models that accurately represent financial entities and member behavior • Champion data quality by implementing checks, validations, and monitoring • Monitor, troubleshoot, and optimize data workflows for performance and maintainability • Contribute to improving data engineering best practices, tooling, and documentation

• Building efficient, critical data pipelines and data platform • Collaborate closely with ML/AI Engineers, Backend Engineers and product to create the data sets that power vidIQ’s algorithms • Be an advocate for data quality, acquisition of new data sources, and data infrastructure tooling • Work closely with cross-functional teammates, including product managers, designers, backend, DevOps and ML/AI Engineers to deliver the highest impact to our users