• Design and implement enterprise data architecture, including data models and integration patterns, to establish a single source of truth for member and donor data. • Implement and manage NetSuite Analytics Warehouse (NSAW) as the primary analytics platform for the organization. • Develop and maintain high-value Power BI dashboards for leadership and operational teams to enable data-driven decision making. • Design and build automated data integration pipelines (ETL/ELT) between NetSuite, M360suite, Klaviyo, and payment gateways. • Define and document data governance policies, including metric definitions, data quality rules, and access controls. • Monitor data pipeline performance, troubleshoot failures, and implement automated error handling to ensure system reliability. • Investigate and resolve data quality incidents (e.g., duplicates, sync failures) and implement monitoring to detect anomalies. • Enable self-service analytics by creating user-friendly data models and training staff on tools to reduce ad-hoc report requests. • Ensure data handling practices comply with relevant regulations (e.g., PCI-DSS) and implement appropriate access controls. • Serve as technical advisor to IT leadership data strategy, recommending technologies and best practices to advance organizational capabilities.

• Design, build, and maintain scalable data pipelines and workflows in Snowflake to support CDP and customer data initiatives. • Integrate and ingest data from multiple systems (CRM, digital analytics, marketing platforms, APIs, flat files, etc.) into Snowflake. • Develop and optimize SQL queries, views, and materialized datasets to support analytics, segmentation, and activation. • Collaborate with data strategy and MarTech teams to translate business requirements into technical data models and pipelines. • Support data standardization and transformation logic for customer profiles, identities, and behavioral data. • Implement and maintain data validation and quality checks within pipelines to ensure data accuracy, consistency, and reliability across ingestion and transformation processes. • Document workflows, schemas, and logic to maintain transparency and support scalability. • Manage and optimize Snowflake environments for performance, cost efficiency, and reliability.

• Collaborate with business and technology stakeholders to understand and capture data requirements, ensuring master data meets user and system needs. • Lead and support the design, development, and enrichment of master data domains to ensure consistency and accuracy. • Configure, implement, and maintain MDM solutions, including data matching, survivorship, and hierarchy management. • Perform hands-on data profiling, cleansing, and enrichment activities to ensure master data quality. • Develop and execute workflows within MDM tools to support business processes. • Oversee governance policies and standards for master data creation, maintenance, and quality. • Develop training materials and provide training to business users about MDM processes, tools, and standards. • Regularly assess the MDM processes, tools, and policies and recommend improvements. • Ensure that master data management processes are compliant with relevant regulations and that data is securely handled.

• Provide primary data engineer for real-time production support, ensuring minimal downtime of mission-critical data pipelines. • Rotate the role and on call requirements. • Monitor, troubleshoot, and optimize data pipeline failures, query performance bottlenecks, and data discrepancies using AWS CloudWatch, Redshift, and PostgreSQL. • Automate Root Cause Analysis (RCA) reporting, reducing resolution time by 30%+ through proactive alerting and system monitoring. • Implement best practices in IAM roles, encryption, and regulatory compliance (HIPAA, GDPR) to ensure data security and governance. • Design, develop, and maintain scalable ETL pipelines using AWS Glue, Lambda, Redshift, S3, and PostgreSQL. • Optimize query performance through partitioning, indexing, query tuning, and materialized views, achieving significant performance improvements. • Implement CI/CD pipelines for automated deployments using AWS CodePipeline, Terraform, and CloudFormation to improve system stability and deployment efficiency. • Support streaming data pipelines using Kafka, Kinesis, or Spark Streaming for real-time data ingestion and processing. • Assist in scaling, performance tuning, and automation to optimize data platform efficiency. • Collaborate with business intelligence teams and analysts to develop high-quality data models that support analytics and decision-making. • Build interactive dashboards and reports using Power BI, Tableau, or Looker to enable real-time insights for stakeholders. • Automate data validation, cleansing, and quality checks, ensuring 99.9%+ data accuracy across critical business functions.