Role Description The Data and Quality Coordinator functions as a vital pooled resource to provide data, regulatory, and quality support for the functional areas within Transplant Services. Working with the transplant data and quality team, the analyst is responsible for ensuring accurate and timely reporting of a high volume of federally mandated registry data across the continuum of transplant care from referral through post-transplant. The coordinator also participates in chart reviews and audits and communicates outcomes directly to leadership, providing areas of focus for optimal compliance and quality improvement. Qualifications - Experience in data management and quality assurance. - Strong analytical skills. - Ability to work collaboratively in a multidisciplinary team. - Excellent communication skills. Requirements - Knowledge of federal regulations related to transplant services. - Experience with chart reviews and audits. - Ability to manage high volumes of data accurately. Benefits - Comprehensive health benefits. - Retirement plans. - Professional development opportunities. - Work-life balance initiatives.

• Rebuild legacy Data Warehouse pipelines in Databricks using PySpark and Spark SQL; • Implement bronze, silver and gold layers following the medallion architecture patterns; • Apply CDC and batch ingestion patterns according to project guidelines; • Execute reconstruction waves by domain, running alongside the legacy DW until cutover; • Implement business rules and transformations with automated tests; • Perform reconciliation and parity validation between legacy environment data and the Lakehouse; • Optimize pipeline performance and costs through partitioning, OPTIMIZE, Z-ORDER and workload sizing; • Contribute to technical documentation for migrated rules; • Support the prioritization and execution of migration waves;

• Develop, optimize, and maintain SQL queries and stored procedures. • Use DBeaver as the primary database management and development tool. • Build and support ETL/ELT processes across the data environment. • Troubleshoot data quality and performance issues. • Partner with application owners and business stakeholders to gather and translate data requirements. • Validate data accuracy and perform testing prior to production deployments. • Document data flows, transformations, and technical specifications. • Participate in knowledge transfer with the internal team throughout the engagement.

• Your main goal will be to build and scale the reliable data infrastructure that powers analytics, AI-driven decision-making, and smarter growth across n8n. • You’ll own critical parts of our modern data stack and help turn growing data needs into trusted, scalable systems: • BUILD AND OWN OUR DATA PIPELINES • Design, build, and own end-to-end data pipelines and transformation workflows using dbt on BigQuery. • Develop scalable ELT models that support analytics, AI, marketing, and operational use cases. • Take ownership of existing pipelines while improving their reliability, maintainability, and performance. • SCALE ORCHESTRATION AND RELIABILITY • Orchestrate and schedule data workflows using Dagster, ensuring pipelines are reliable, observable, and easy to operate. • Build robust testing, monitoring, alerting, and recovery processes across the data platform. • Improve pipeline architecture as workloads, data volumes, and business-critical use cases grow. • ENABLE ANALYTICS AND AI USE CASES • Partner with analysts and data scientists to deliver trusted, well-modeled, self-serve datasets in Hex. • Integrate and operationalize AI and LLM-driven workflows within the data platform. • Help internal teams move from ad-hoc data requests to scalable and reusable data products. • POWER MARKETING ATTRIBUTION AND ACTIVATION • Build marketing attribution models that improve visibility into campaign performance and customer conversion. • Develop reverse ETL workflows that send accurate conversion data back to platforms such as Google Ads and Meta. • Integrate external data sources, advertising APIs, and data vendors into reliable production pipelines. • SET DATA PLATFORM STANDARDS • Establish and enforce standards for data quality, testing, documentation, observability, and deployment. • Optimize BigQuery workloads for query performance, scalability, and cost efficiency. • Contribute to data architecture decisions, mentor future hires, and help shape the technical direction of the data engineering function.