• Design, develop, and optimize scalable **ETL/ELT pipelines** for enterprise data platforms • Build and maintain robust data solutions using **Snowflake, Python, and AWS services** • Develop advanced SQL queries using **CTEs, window functions, query tuning, and performance optimization techniques** • Work with **Snowflake warehouses, tasks, streams, and data pipelines** for efficient data processing • Create and maintain data transformation workflows using **dbt (data build tool)** • Develop reporting datasets and support dashboarding solutions using **Amazon QuickSight** • Perform **data validation, data integrity checks, and root cause analysis** to ensure data accuracy and reliability • Integrate and manage data across platforms including **DynamoDB, S3, AWS Glue, Lambda, and Rockset** • Collaborate with cross-functional teams including analytics, product, and clinical stakeholders to support reporting requirements • Monitor pipeline performance and troubleshoot production data issues

• Architecture & Solution Design • Own full lifecycle delivery: requirements → design → build → deploy → adoption → optimization & support • Ensure performance, scalability, and reliability across EDW and BI platforms • Drive cloud deployment and production readiness of analytics solutions • Facilitate architecture and technical design sessions • Act as a trusted advisor bridging business strategy and technical delivery • Lead and mentor BI developers, data engineers, and analysts • Establish and enforce data governance, data stewardship, security, and reporting standards

• Architect complex, high-volume data pipelines for production use. • Design and implement scalable data models serving multiple product and internal teams. • Own data quality frameworks and standards across key data products. • Build reusable patterns for transformations and metrics to drive efficiency. • Define and maintain core business metrics and Key Performance Indicators (KPIs) in partnership with Analytics. • Own the data products used across the company, ensuring reliability and performance. • Set and promote standards for data modeling and pipeline development. • Partner closely with Analytics, Data Science, and Machine Learning teams on requirements to reduce friction and accelerate their work. • Mentor engineers and actively participate in the hiring process.

• Design, develop, and maintain scalable data pipelines and analytics solutions in GCP. • Develop solutions for collecting, processing, and analyzing LLM token usage data. • Build forecasting and anomaly detection mechanisms to monitor AI consumption patterns. • Optimize and maintain BigQuery datasets and cloud-based processing environments. • Collaborate with cross-functional teams to translate business needs into scalable AI/data solutions. • Ensure high-quality data processing through testing, monitoring, and automation best practices. • Contribute to data architecture, analytics frameworks, and continuous improvement initiatives.