• Lead architecture assessment and documentation of the current data platform (Snowflake, Airflow, AWS) • Design the future-state data platform architecture following modern ELT and dbt best practices • Define standards for: data modeling, pipeline architecture, transformation frameworks, governance and data quality • Lead the migration of transformation logic into dbt models • Define architectural patterns for: staging layers, intermediate layers, data marts • Implement automated testing frameworks within dbt • Lead discovery sessions and technical workshops with the client’s data engineering teams • Document architecture, pipelines, and operational processes • Conduct architecture walkthrough sessions during knowledge transfer phases • Define standards for CI/CD, monitoring frameworks, and data quality validation • Establish schema management and governance best practices • Optimize Snowflake compute usage and platform performance • Design scalable orchestration strategies for Airflow pipelines • Improve platform reliability, monitoring, and operational observability

• 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