• Own the foundation of the data for AI systems • Join a high-output engineering team focusing on pipelines, data models, and platform infrastructure • Work closely with data scientists, software engineers, and product teams to ensure data accuracy, observability, and scalability • Design data architecture to comply with regulations in a financial environment • Govern AI-generated pipeline code and ensure standards for production safety • Ensure data quality end-to-end, identify root causes, and maintain platforms that evolve as data investments grow

• Design and implement a scalable lakehouse medallion architecture within Databricks. • Establish ingestion, canonical, and business-ready data modeling layers with clear standards and naming conventions. • Build and maintain structured, governed fact and dimension models for enterprise reporting. • Oversee ingestion pipelines using Fivetran, Airbyte, and custom integrations while optimizing cost and performance. • Develop and manage SQL and Python transformation jobs including incremental processing, upsert logic, and snapshot modeling. • Implement orchestration, monitoring, data quality validation, and failure recovery processes. • Define enterprise data governance standards including access controls and reconciliation processes. • Integrate ERP, Shopify, GA4, Google Ads, Meta Ads, and catalog marketing data into unified customer and order models. • Lead and mentor a small team of data and analytics engineers while setting coding and review standards.

• Collaborate with the Team Lead and cross‑functional teams to gather and refine data requirements for Denials AI solutions • Design, implement, and optimize ETL/ELT pipelines using Python, Dagster, DBT, and AWS data services (Athena, Glue, SQS) • Develop and maintain data models in PostgreSQL; write efficient SQL for querying and performance tuning • Monitor pipeline health and performance; troubleshoot data incidents and implement preventive measures • Enforce data quality and governance standards, including HIPAA compliance for PHI handling • Conduct code reviews, share best practices, and mentor junior data engineers • Automate deployment and monitoring tasks using infrastructure-as-code and AWS CloudWatch metrics and alarms • Document data workflows, schemas, and operational runbooks to support team knowledge transfer

• Collaborate with the Team Lead and cross‑functional teams to gather and refine data requirements for Denials AI solutions • Design, implement, and optimize ETL/ELT pipelines using Python, Dagster, DBT, and AWS data services (Athena, Glue, SQS) • Develop and maintain data models in PostgreSQL; write efficient SQL for querying and performance tuning • Monitor pipeline health and performance; troubleshoot data incidents and implement preventive measures • Enforce data quality and governance standards, including HIPAA compliance for PHI handling • Conduct code reviews, share best practices, and mentor junior data engineers • Automate deployment and monitoring tasks using infrastructure-as-code and AWS CloudWatch metrics and alarms • Document data workflows, schemas, and operational runbooks to support team knowledge transfer